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Chang, Jack, Dhara Patel, Kimberly C. Claeys, Marc H. Scheetz, and Emily Heil. "1090. Does calculation method matter for targeting vancomycin AUC?" Open Forum Infectious Diseases 8, Supplement_1 (2021): S636. http://dx.doi.org/10.1093/ofid/ofab466.1284.
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Abstract Background Recent vancomycin (VAN) guidelines recommend targeting an area under the curve (AUC) concentration of 400-600 for treatment of methicillin resistant Staphylococcus aureus infections. Multiple strategies for calculating AUC exist, including first order pharmacokinetic (foPK) equations and Bayesian models. Most clinical applications of foPK assume unchanged patient status and project ideal administration times to estimate exposure. Bayesian modeling provides the best estimate of true drug exposure and can incorporate changing patient covariates and exact doses. We compared two commonly used foPK methods to Bayesian estimates of VAN AUC. Graphs depict calculated AUCs using the three different methods: 1) Population PK estimated (foPOPPK) 2) Two-level first dose estimated (foFDPK) 3) Bayesian estimated. Methods First order equations were performed using population PK estimates (foPOPPK) to estimate steady state (SS) AUC and initial doses. Two concentrations after first dose were used to estimate SS AUC (foFDPK). A 2-compartment Bayesian model allometrically scaled for weight and adjusted for creatinine clearance was used to determine 24-48 hour AUCs. Differences between AUCs were compared using a mixed-effects analysis, and correlation of foPK equations to Bayesian estimates was described using Spearman’s correlation. Patient results from each method were classified as below (< 400), within (400-600), or above ( >600) targets. Results 65 adult patients were included. The median and IQR for calculated AUCs using foPOPPK, foFDPK, and Bayesian methods were 495.6 (IQR: 76.6), 498.2 (IQR: 107.4), and 472.1 (IQR: 177.9), respectively with p >0.65 for both foPK methods vs. the Bayesian method. AUCs predicted by foPK equations were poorly correlated with Bayesian AUCs (Spearman’s rho= -0.08, p=0.55), while AUCs from foFDPK better correlated with Bayesian AUCs (Spearman’s rho= 0.48, p=0.00). AUCs were within, above, and below target for 54%, 20%, and 26% for the Bayesian model; 95%, 5% and 0% for foPOPPK; and 74%, 12%, and 14% for foFDPK. foPK AUC estimates concurred with Bayesian estimates only 52% of the time. Conclusion AUCs calculated by the three methods did not differ on average, but dosing recommendations for foPK at the patient level varied substantially compared to the Bayesian method. This difference is because Bayesian estimation incorporates actual patient exposures while foPK equations rely on idealized dose timing to predict AUCs. Disclosures Kimberly C. Claeys, PharmD, GenMark (Speaker’s Bureau) Marc H. Scheetz, PharmD, MSc, Nevakar (Grant/Research Support)SuperTrans Medical (Consultant)US Patent #10688195B2 (Other Financial or Material Support, Patent holder)
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Rahman, Mohammad Lutfor, Steven G. Gilmour, Peter J. Zemroch, and Pauline R. Ziman. "Bayesian analysis of fuel economy experiments." Journal of Statistical Research 54, no. 1 (2020): 43–63. http://dx.doi.org/10.47302/jsr.2020540103.
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Statistical analysts can encounter difficulties in obtaining point and interval estimates for fixed effects when sample sizes are small and there are two or more error strata to consider. Standard methods can lead to certain variance components being estimated as zero which often seems contrary to engineering experience and judgement. Shell Global Solutions (UK) has encountered such challenges and is always looking for ways to make its statistical techniques as robust as possible. In this instance, the challenge was to estimate fuel effects and confidence limits from small-sample fuel economy experiments where both test-to-test and day-to-day variation had to be taken into account. Using likelihood-based methods, the experimenters estimated the day-to-day variance component to be zero which was unrealistic. The reason behind this zero estimate is that the data set is not large enough to estimate it reliably. The experimenters were also unsure about the fixed parameter estimates obtained by likelihood methods in linear mixed models. In this paper, we looked for an alternative to compare the likelihood estimates against and found the Bayesian platform to be appropriate. Bayesian methods assuming some non-informative and weakly informative priors enable us to compare the parameter estimates and the variance components. Profile likelihood and bootstrap based methods verified that the Bayesian point and interval estimates were not unreasonable. Also, simulation studies have assessed the quality of likelihood and Bayesian estimates in this study.
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Sanger, Terence D. "Bayesian Filtering of Myoelectric Signals." Journal of Neurophysiology 97, no. 2 (2007): 1839–45. http://dx.doi.org/10.1152/jn.00936.2006.
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Surface electromyography is used in research, to estimate the activity of muscle, in prosthetic design, to provide a control signal, and in biofeedback, to provide subjects with a visual or auditory indication of muscle contraction. Unfortunately, successful applications are limited by the variability in the signal and the consequent poor quality of estimates. I propose to use a nonlinear recursive filter based on Bayesian estimation. The desired filtered signal is modeled as a combined diffusion and jump process and the measured electromyographic (EMG) signal is modeled as a random process with a density in the exponential family and rate given by the desired signal. The rate is estimated on-line by calculating the full conditional density given all past measurements from a single electrode. The Bayesian estimate gives the filtered signal that best describes the observed EMG signal. This estimate yields results with very low short-time variability but also with the capability of very rapid response to change. The estimate approximates isometric joint torque with lower error and higher signal-to-noise ratio than current linear methods. Use of the nonlinear filter significantly reduces noise compared with current algorithms, and it may therefore permit more effective use of the EMG signal for prosthetic control, biofeedback, and neurophysiology research.
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Roy, Himadri Shekhar, Amrit Kumar Paul, Ranjit Kumar Paul, Ramesh Kumar Singh, MD `YEASIN, and Prakash Kumar. "Estimation of Heritability of Karan Fries Cattle using Bayesian Procedure." Indian Journal of Animal Sciences 92, no. 5 (2022): 645–48. http://dx.doi.org/10.56093/ijans.v92i5.117167.
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  The Bayesian model was applied for analyzing the first lactation in Karan Fries cattle. First lactation data of production (305-day or less milk yield and daily milk yield) were collected from the history-cum pedigree sheet and daily milk yield registers of the division of Dairy Cattle Breeding (DCB), National Dairy Research Institute (NDRI), Karnal. In the Bayesian paradigm, MCMC methods are applied to solve complex mathematical problems to estimate a large number of unknown parameters. Assuming linear mixed model and using the different prior set up, diagnostic of MCMC (Markov Chain Monte Carlo) was carried out graphically as well as by Heidelberg stationarity test. Variance estimates of the random effects (VA) and residual variance estimation (VR) and Variance estimate location effects i.e. fixed effects were calculated along with effective sample size. Finally, heritability (h2) estimates for First lactation 305 days or less milk yield (FL305DMY) was estimated along with its credible interval.
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Christ, Theodore J., and Christopher David Desjardins. "Curriculum-Based Measurement of Reading: An Evaluation of Frequentist and Bayesian Methods to Model Progress Monitoring Data." Journal of Psychoeducational Assessment 36, no. 1 (2017): 55–73. http://dx.doi.org/10.1177/0734282917712174.
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Curriculum-Based Measurement of Oral Reading (CBM-R) is often used to monitor student progress and guide educational decisions. Ordinary least squares regression (OLSR) is the most widely used method to estimate the slope, or rate of improvement (ROI), even though published research demonstrates OLSR’s lack of validity and reliability, and imprecision of ROI estimates, especially after brief duration of monitoring (6-10 weeks). This study illustrates and examines the use of Bayesian methods to estimate ROI. Conditions included four progress monitoring durations (6, 8, 10, and 30 weeks), two schedules of data collection (weekly, biweekly), and two ROI growth distributions that broadly corresponded with ROIs for general and special education populations. A Bayesian approach with alternate prior distributions for the ROIs is presented and explored. Results demonstrate that Bayesian estimates of ROI were more precise than OLSR with comparable reliabilities, and Bayesian estimates were consistently within the plausible range of ROIs in contrast to OLSR, which often provided unrealistic estimates. Results also showcase the influence the priors had estimated ROIs and the potential dangers of prior distribution misspecification.
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Fässler, Sascha M. M., Andrew S. Brierley, and Paul G. Fernandes. "A Bayesian approach to estimating target strength." ICES Journal of Marine Science 66, no. 6 (2009): 1197–204. http://dx.doi.org/10.1093/icesjms/fsp008.
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Abstract Fässler, S. M. M., Brierley, A. S., and Fernandes, P. G. 2009. A Bayesian approach to estimating target strength. – ICES Journal of Marine Science, 66: 1197–1204. Currently, conventional models of target strength (TS) vs. fish length, based on empirical measurements, are used to estimate fish density from integrated acoustic data. These models estimate a mean TS, averaged over variables that modulate fish TS (tilt angle, physiology, and morphology); they do not include information about the uncertainty of the mean TS, which could be propagated through to estimates of fish abundance. We use Bayesian methods, together with theoretical TS models and in situ TS data, to determine the uncertainty in TS estimates of Atlantic herring (Clupea harengus). Priors for model parameters (surface swimbladder volume, tilt angle, and s.d. of the mean TS) were used to estimate posterior parameter distributions and subsequently build a probabilistic TS model. The sensitivity of herring abundance estimates to variation in the Bayesian TS model was also evaluated. The abundance of North Sea herring from the area covered by the Scottish acoustic survey component was estimated using both the conventional TS–length formula (5.34×109 fish) and the Bayesian TS model (mean = 3.17×109 fish): this difference was probably because of the particular scattering model employed and the data used in the Bayesian model. The study demonstrates the relative importance of potential bias and precision of TS estimation and how the latter can be so much less important than the former.
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Alharbi, Yasser S., and Amr R. Kamel. "Fuzzy System Reliability Analysis for Kumaraswamy Distribution: Bayesian and Non-Bayesian Estimation with Simulation and an Application on Cancer Data Set." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 19 (June 7, 2022): 118–39. http://dx.doi.org/10.37394/23208.2022.19.14.
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This paper proposes the fuzzy Bayesian (FB) estimation to get the best estimate of the unknown parameters of a two-parameter Kumaraswamy distribution from a frequentist point of view. These estimations of parameters are employed to estimate the fuzzy reliability function of the Kumaraswamy distribution and to select the best estimate of the parameters and fuzzy reliability function. To achieve this goal we investigate the efficiency of seven classical estimators and compare them with FB proposed estimation. Monte Carlo simulations and cancer data set applications are performed to compare the performances of the estimators for both small and large samples. Tierney and Kadane approximation is used to obtain FB estimates of traditional and fuzzy reliability for the Kumaraswamy distribution. The results showed that the fuzziness is better than the reality for all sample sizes and the fuzzy reliability at the estimates of the FB proposed estimated is better than other estimators, it gives the lowest Bias and root mean squared error.
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Ambrose, Paul G., Jeffrey P. Hammel, Sujata M. Bhavnani, Christopher M. Rubino, Evelyn J. Ellis-Grosse, and George L. Drusano. "Frequentist and Bayesian Pharmacometric-Based Approaches To Facilitate Critically Needed New Antibiotic Development: Overcoming Lies, Damn Lies, and Statistics." Antimicrobial Agents and Chemotherapy 56, no. 3 (2011): 1466–70. http://dx.doi.org/10.1128/aac.01743-10.
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ABSTRACTAntimicrobial drug development has greatly diminished due to regulatory uncertainty about the magnitude of the antibiotic treatment effect. Herein we evaluate the utility of pharmacometric-based analyses for determining the magnitude of the treatment effect. Frequentist and Bayesian pharmacometric-based logistic regression analyses were conducted by using data from a phase 3 clinical trial of tigecycline-treated patients with hospital-acquired pneumonia (HAP) to evaluate relationships between the probability of microbiological or clinical success and the free-drug area under the concentration-time curve from time zero to 24 h (AUC0-24)/MIC ratio. By using both the frequentist and Bayesian approaches, the magnitude of the treatment effect was determined using three different methods based on the probability of success at free-drug AUC0-24/MIC ratios of 0.01 and 25. Differences in point estimates of the treatment effect for microbiological response (method 1) were larger using the frequentist approach than using the Bayesian approach (Bayesian estimate, 0.395; frequentist estimate, 0.637). However, the Bayesian credible intervals were tighter than the frequentist confidence intervals, demonstrating increased certainty with the former approach. The treatment effect determined by taking the difference in the probabilities of success between the upper limit of a 95% interval for the minimal exposure and the lower limit of a 95% interval at the maximal exposure (method 2) was greater for the Bayesian analysis (Bayesian estimate, 0.074; frequentist estimate, 0.004). After utilizing bootstrapping to determine the lower 95% bounds for the treatment effect (method 3), treatment effect estimates were still higher for the Bayesian analysis (Bayesian estimate, 0.301; frequentist estimate, 0.166). These results demonstrate the utility of frequentist and Bayesian pharmacometric-based analyses for the determination of the treatment effect using contemporary trial endpoints. Additionally, as demonstrated by using pharmacokinetic-pharmacodynamic data, the magnitude of the treatment effect for patients with HAP is large.
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Ben Zaabza, Hafedh, Abderrahmen Ben Gara, Hedi Hammami, Mohamed Amine Ferchichi, and Boulbaba Rekik. "Estimation of variance components of milk, fat, and protein yields of Tunisian Holstein dairy cattle using Bayesian and REML methods." Archives Animal Breeding 59, no. 2 (2016): 243–48. http://dx.doi.org/10.5194/aab-59-243-2016.
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Abstract. A multi-trait repeatability animal model under restricted maximum likelihood (REML) and Bayesian methods was used to estimate genetic parameters of milk, fat, and protein yields in Tunisian Holstein cows. The estimates of heritability for milk, fat, and protein yields from the REML procedure were 0.21 ± 0.05, 0.159 ± 0.04, and 0.158 ± 0.04, respectively. The corresponding results from the Bayesian procedure were 0.273 ± 0.02, 0.198 ± 0.01, and 0.187 ± 0.01. Heritability estimates tended to be larger via the Bayesian than those obtained by the REML method. Genetic and permanent environmental variances estimated by REML were smaller than those obtained by the Bayesian analysis. Inversely, REML estimates of the residual variances were larger than Bayesian estimates. Genetic and permanent correlation estimates were on the other hand comparable by both REML and Bayesian methods with permanent environmental being larger than genetic correlations. Results from this study confirm previous reports on genetic parameters for milk traits in Tunisian Holsteins and suggest that a multi-trait approach can be an alternative for implementing a routine genetic evaluation of the Tunisian dairy cattle population.
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Chen, Ziqi, Cameron Fackler, and Ning Xiang. "Bayesian Parameter estimation of microphone positions, sound speed and dissipation for impedance tube measurements." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 7 (2023): 503–7. http://dx.doi.org/10.3397/in_2022_0070.
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With tube measurement widely used for acoustic measurements, calibration plays an important role in verifying and validating the measurement. This work applies a Bayesian method based on an air layer reflectance model to estimate the microphone positions, and sound speed in consideration of environmental effects on uncertainties of the normal incident impedance tube measurements. Bayesian theorem is applied to estimate the microphone positions and sound speed given the experimental data obtained from the transfer function method (TFM) in tube measurements. With a hypothetical air layer treated as material under test in front of a rigid backing in the tube, a parametric model is established for the TFM tube measurement to estimate the microphone positions using Bayesian inference. With the microphone positions accurately estimated, sound speed and losses due to tube interior boundary effects are also estimated within the same Bayesian framework. Bayesian analysis results show that Bayesian parameter estimation based on the air layer model is well suited in estimating the sound speed, microphone positions, and other parameters to ensure highly accurate tube measurements.
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Dhiraj Kumar Singh and Himanshu Bhatt. "Estimates of some entropies and their relevance in financial market." Gulf Journal of Mathematics 20 (June 14, 2025): 222–41. https://doi.org/10.56947/gjom.v20i.2820.
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Using various types of bivariate beta priors such as bivariate beta type-I prior, Conner and Mosimann bivariate beta prior and bivariate beta type-III prior, the Bayesian estimate of the entropy of type-(α) and entropy of type-(α,β) are derived for multinomial likelihood. Bayesian estimates of Shannon entropy for different bivariate beta priors can be represented as a limiting function of Bayesian estimates of entropy of type-(α). In this paper, the parameters of the bivariate beta type-III prior under multinomial likelihood in Moody's corporate bond default rates data are selected using the Bayesian estimate of the entropy type -(α).
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Okasha, Hassan, Yuhlong Lio, and Mohammed Albassam. "On Reliability Estimation of Lomax Distribution under Adaptive Type-I Progressive Hybrid Censoring Scheme." Mathematics 9, no. 22 (2021): 2903. http://dx.doi.org/10.3390/math9222903.
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Bayesian estimates involve the selection of hyper-parameters in the prior distribution. To deal with this issue, the empirical Bayesian and E-Bayesian estimates may be used to overcome this problem. The first one uses the maximum likelihood estimate (MLE) procedure to decide the hyper-parameters; while the second one uses the expectation of the Bayesian estimate taken over the joint prior distribution of the hyper-parameters. This study focuses on establishing the E-Bayesian estimates for the Lomax distribution shape parameter functions by utilizing the Gamma prior of the unknown shape parameter along with three distinctive joint priors of Gamma hyper-parameters based on the square error as well as two asymmetric loss functions. These two asymmetric loss functions include a general entropy and LINEX loss functions. To investigate the effect of the hyper-parameters’ selections, mathematical propositions have been derived for the E-Bayesian estimates of the three shape functions that comprise the identity, reliability and hazard rate functions. Monte Carlo simulation has been performed to compare nine E-Bayesian, three empirical Bayesian and Bayesian estimates and MLEs for any aforementioned functions. Additionally, one simulated and two real data sets from industry life test and medical study are applied for the illustrative purpose. Concluding notes are provided at the end.
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AGBAJE, Olorunsola F., Stephen D. LUZIO, Ahmed I. S. ALBARRAK, David J. LUNN, David R. OWENS, and Roman HOVORKA. "Bayesian hierarchical approach to estimate insulin sensitivity by minimal model." Clinical Science 105, no. 5 (2003): 551–60. http://dx.doi.org/10.1042/cs20030117.
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We adopted Bayesian analysis in combination with hierarchical (population) modelling to estimate simultaneously population and individual insulin sensitivity (SI) and glucose effectiveness (SG) with the minimal model of glucose kinetics using data collected during insulin-modified intravenous glucose tolerance test (IVGTT) and made comparison with the standard non-linear regression analysis. After fasting overnight, subjects with newly presenting Type II diabetes according to World Health Organization criteria (n=65; 53 males, 12 females; age, 54±9 years; body mass index, 30.4±5.2 kg/m2; means±S.D.) underwent IVGTT consisting of a 0.3 g of glucose bolus/kg of body weight given at time zero for 2 min, followed by 0.05 unit of insulin/kg of body weight at 20 min. Bayesian inference was carried out using vague prior distributions and log-normal distributions to guarantee non-negativity and, thus, physiological plausibility of model parameters and associated credible intervals. Bayesian analysis gave estimates of SI in all subjects. Non-linear regression analysis failed in four cases, where Bayesian analysis-derived SI was located in the lower quartile and was estimated with lower precision. The population means of SI and SG provided by Bayesian analysis and non-linear regression were identical, but the interquartile range given by Bayesian analysis was tighter by approx. 20% for SI and by approx. 15% for SG. Individual insulin sensitivities estimated by the two methods were highly correlated (rS=0.98; P<0.001). However, the correlation in the lower 20% centile of the insulin-sensitivity range was significantly lower than the correlation in the upper 80% centile (rS=0.71 compared with rS=0.99; P<0.001). We conclude that the Bayesian hierarchical analysis is an appealing method to estimate SI and SG, as it avoids parameter estimation failures, and should be considered when investigating insulin-resistant subjects.
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MOLINARES, CARLOS A., and CHRIS P. TSOKOS. "BAYESIAN RELIABILITY APPROACH TO THE POWER LAW PROCESS WITH SENSITIVITY ANALYSIS TO PRIOR SELECTION." International Journal of Reliability, Quality and Safety Engineering 20, no. 01 (2013): 1350004. http://dx.doi.org/10.1142/s0218539313500046.
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The intensity function is the key entity to the power law process, also known as the Weibull process or nonhomogeneous Poisson process. It gives the rate of change of the reliability of a system as a function of time. We illustrate that a Bayesian analysis is applicable to the power law process through the intensity function. First, we show using real data, that one of the two parameters in the intensity function behaves as a random variable. With a sequence of estimates of the subject parameter we proceeded to identify the probability distribution that characterizes its behavior. Using the commonly used squared-error loss function we obtain a Bayesian reliability estimate of the power law process. Also a simulation procedure shows the superiority of the Bayesian estimate with respect to the maximum likelihood estimate and the better performance of the proposed estimate with respect to its maximum likelihood counterpart. As well, it was found that the Bayesian estimate is sensitive to a prior selection.
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Zhang, Qingyang, and Xuan Shi. "A mixture copula Bayesian network model for multimodal genomic data." Cancer Informatics 16 (January 1, 2017): 117693511770238. http://dx.doi.org/10.1177/1176935117702389.
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Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes the decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when the normality assumption is moderately or severely violated, making it unsuitable for dealing with recent genomic data such as the Cancer Genome Atlas data. In the present paper, we propose a mixture copula Bayesian network model which provides great flexibility in modeling non-Gaussian and multimodal data for causal inference. The parameters in mixture copula functions can be efficiently estimated by a routine expectation–maximization algorithm. A heuristic search algorithm based on Bayesian information criterion is developed to estimate the network structure, and prediction can be further improved by the best-scoring network out of multiple predictions from random initial values. Our method outperforms Gaussian Bayesian networks and regular copula Bayesian networks in terms of modeling flexibility and prediction accuracy, as demonstrated using a cell signaling data set. We apply the proposed methods to the Cancer Genome Atlas data to study the genetic and epigenetic pathways that underlie serous ovarian cancer.
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Richard, Michael D., and Richard P. Lippmann. "Neural Network Classifiers Estimate Bayesian a posteriori Probabilities." Neural Computation 3, no. 4 (1991): 461–83. http://dx.doi.org/10.1162/neco.1991.3.4.461.
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Many neural network classifiers provide outputs which estimate Bayesian a posteriori probabilities. When the estimation is accurate, network outputs can be treated as probabilities and sum to one. Simple proofs show that Bayesian probabilities are estimated when desired network outputs are 1 of M (one output unity, all others zero) and a squared-error or cross-entropy cost function is used. Results of Monte Carlo simulations performed using multilayer perceptron (MLP) networks trained with backpropagation, radial basis function (RBF) networks, and high-order polynomial networks graphically demonstrate that network outputs provide good estimates of Bayesian probabilities. Estimation accuracy depends on network complexity, the amount of training data, and the degree to which training data reflect true likelihood distributions and a priori class probabilities. Interpretation of network outputs as Bayesian probabilities allows outputs from multiple networks to be combined for higher level decision making, simplifies creation of rejection thresholds, makes it possible to compensate for differences between pattern class probabilities in training and test data, allows outputs to be used to minimize alternative risk functions, and suggests alternative measures of network performance.
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Tansitpong, Praowpan. "Probabilistic Model of Patient Classification Using Bayesian Model." International Journal of Reliable and Quality E-Healthcare 13, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijrqeh.348579.
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The research emphasizes the effectiveness of Bayesian classification algorithms in predicting patient visits in healthcare settings. Bayesian algorithms examine past patient data to detect intricate patterns in admission dynamics, including demographic, clinical, and temporal factors. Through the use of Bayesian principles, prediction models are able to estimate the probability of certain patient demographics occurring at certain intervals, therefore assisting in the allocation of resources and the management of operations. Probabilities that have been estimated are used to make choices on staffing, resource allocation, and operational strategy. The variation in probability estimates across different observations improves the predictive usefulness, hence strengthening the effectiveness in healthcare management and planning.
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Wei, Cheng Dong, Fu Wang, and Huan Qi Wei. "Bayesian Estimate of Exponential Parameter with Missing Data." Applied Mechanics and Materials 321-324 (June 2013): 904–8. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.904.
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We discuss the empirical Bayesian estimation and the noninformative prior Bayesian estimation of Exponential parameter in the missing data occasion. By setting different prior distributions, we get different bayesian risks and compare the numerical simulation results through the MATLAB programming.
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Al-Hossain, Abdullah Y. "Burr-X Model Estimate using Bayesian and non-Bayesian Approaches." Journal of Mathematics and Statistics 12, no. 2 (2016): 77–85. http://dx.doi.org/10.3844/jmssp.2016.77.85.
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Lau, John W., Tak Kuen Siu, and Hailiang Yang. "On Bayesian Mixture Credibility." ASTIN Bulletin 36, no. 02 (2006): 573–88. http://dx.doi.org/10.2143/ast.36.2.2017934.
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We introduce a class of Bayesian infinite mixture models first introduced by Lo (1984) to determine the credibility premium for a non-homogeneous insurance portfolio. The Bayesian infinite mixture models provide us with much flexibility in the specification of the claim distribution. We employ the sampling scheme based on a weighted Chinese restaurant process introduced in Lo et al. (1996) to estimate a Bayesian infinite mixture model from the claim data. The Bayesian sampling scheme also provides a systematic way to cluster the claim data. This can provide some insights into the risk characteristics of the policyholders. The estimated credibility premium from the Bayesian infinite mixture model can be written as a linear combination of the prior estimate and the sample mean of the claim data. Estimation results for the Bayesian mixture credibility premiums will be presented.
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Lau, John W., Tak Kuen Siu, and Hailiang Yang. "On Bayesian Mixture Credibility." ASTIN Bulletin 36, no. 2 (2006): 573–88. http://dx.doi.org/10.1017/s0515036100014677.
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We introduce a class of Bayesian infinite mixture models first introduced by Lo (1984) to determine the credibility premium for a non-homogeneous insurance portfolio. The Bayesian infinite mixture models provide us with much flexibility in the specification of the claim distribution. We employ the sampling scheme based on a weighted Chinese restaurant process introduced in Lo et al. (1996) to estimate a Bayesian infinite mixture model from the claim data. The Bayesian sampling scheme also provides a systematic way to cluster the claim data. This can provide some insights into the risk characteristics of the policyholders. The estimated credibility premium from the Bayesian infinite mixture model can be written as a linear combination of the prior estimate and the sample mean of the claim data. Estimation results for the Bayesian mixture credibility premiums will be presented.
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Lehky, Sidney R. "Bayesian Estimation of Stimulus Responses in Poisson Spike Trains." Neural Computation 16, no. 7 (2004): 1325–43. http://dx.doi.org/10.1162/089976604323057407.
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A Bayesian method is developed for estimating neural responses to stimuli, using likelihood functions incorporating the assumption that spike trains follow either pure Poisson statistics or Poisson statistics with a refractory period. The Bayesian and standard estimates of the mean and variance of responses are similar and asymptotically converge as the size of the data sample increases. However, the Bayesian estimate of the variance of the variance is much lower. This allows the Bayesian method to provide more precise interval estimates of responses. Sensitivity of the Bayesian method to the Poisson assumption was tested by conducting simulations perturbing the Poisson spike trains with noise. This did not affect Bayesian estimates of mean and variance to a significant degree, indicating that the Bayesian method is robust. The Bayesian estimates were less affected by the presence of noise than estimates provided by the standard method.
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Rubilar-Torrealba, Rolando, Karime Chahuán-Jiménez, Hanns de la Fuente-Mella, and Claudio Elórtegui-Gómez. "Bayesian Approach to Stochastic Estimation of Population Survival Curves in Chile Using ABC Techniques and Its Impact over Social Structures." Computation 12, no. 8 (2024): 154. http://dx.doi.org/10.3390/computation12080154.
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In Chile and worldwide, life expectancy has consistently increased over the past six decades. Thus, the purpose of this study was to identify, measure, and estimate the population mortality ratios in Chile, mortality estimates are used to calculate life expectancy when constructing life tables. The Bayesian approach, specifically through Approximate Bayesian Computation (ABC) is employed to optimize parameter selection for these calculations. ABC corresponds to a class of computational methods rooted in Bayesian statistics that could be used to estimate the posterior distributions of the model parameters. For this research, ABC was applied to estimate the mortality ratios in Chile, using information available from 2004 to 2021. The results showed heterogeneity in the results when selecting the best model. Additionally, it was possible to generate projections for the next 10 years for the series analysed in the research. Finally, the main contribution of this research is that we measured and estimated the population mortality rates in Chile, defining the optimal selection of parameters, in order to contribute to creating a link between social and technical sciences for the advancement and implementation of current knowledge in the field of social structures.
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Emelyanov, V. E., and S. P. Matyuk. "BAYESIAN ESTIMATE OF TELECOMMUNICATION SYSTEMS PREPAREDNESS." Civil Aviation High Technologies 24, no. 1 (2021): 16–22. http://dx.doi.org/10.26467/2079-0619-2021-24-1-16-22.
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Phang, Sen, Pietro Ravani, Jeffrey Schaefer, Bruce Wright, and Kevin Mclaughlin. "Internal Medicine residents use heuristics to estimate disease probability." Canadian Medical Education Journal 6, no. 2 (2015): e71-e77. http://dx.doi.org/10.36834/cmej.36653.
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Background: Training in Bayesian reasoning may have limited impact on accuracy of probability estimates. In this study, our goal was to explore whether residents previously exposed to Bayesian reasoning use heuristics rather than Bayesian reasoning to estimate disease probabilities. We predicted that if residents use heuristics then post-test probability estimates would be increased by non-discriminating clinical features or a high anchor for a target condition.Method: We randomized 55 Internal Medicine residents to different versions of four clinical vignettes and asked them to estimate probabilities of target conditions. We manipulated the clinical data for each vignette to be consistent with either 1) using a representative heuristic, by adding non-discriminating prototypical clinical features of the target condition, or 2) using anchoring with adjustment heuristic, by providing a high or low anchor for the target condition.Results: When presented with additional non-discriminating data the odds of diagnosing the target condition were increased (odds ratio (OR) 2.83, 95% confidence interval [1.30, 6.15], p = 0.009). Similarly, the odds of diagnosing the target condition were increased when a high anchor preceded the vignette (OR 2.04, [1.09, 3.81], p = 0.025).Conclusions: Our findings suggest that despite previous exposure to the use of Bayesian reasoning, residents use heuristics, such as the representative heuristic and anchoring with adjustment, to estimate probabilities. Potential reasons for attribute substitution include the relative cognitive ease of heuristics vs. Bayesian reasoning or perhaps residents in their clinical practice use gist traces rather than precise probability estimates when diagnosing.
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Okasha, Hassan M., Heba S. Mohammed, and Yuhlong Lio. "E-Bayesian Estimation of Reliability Characteristics of a Weibull Distribution with Applications." Mathematics 9, no. 11 (2021): 1261. http://dx.doi.org/10.3390/math9111261.
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Given a progressively type-II censored sample, the E-Bayesian estimates, which are the expected Bayesian estimates over the joint prior distributions of the hyper-parameters in the gamma prior distribution of the unknown Weibull rate parameter, are developed for any given function of unknown rate parameter under the square error loss function. In order to study the impact from the selection of hyper-parameters for the prior, three different joint priors of the hyper-parameters are utilized to establish the theoretical properties of the E-Bayesian estimators for four functions of the rate parameter, which include an identity function (that is, a rate parameter) as well as survival, hazard rate and quantile functions. A simulation study is also conducted to compare the three E-Bayesian and a Bayesian estimate as well as the maximum likelihood estimate for each of the four functions considered. Moreover, two real data sets from a medical study and industry life test, respectively, are used for illustration. Finally, concluding remarks are addressed.
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Cu Thi, Phuong, James Ball, and Ngoc Dao. "Uncertainty Estimation Using the Glue and Bayesian Approaches in Flood Estimation: A case Study—Ba River, Vietnam." Water 10, no. 11 (2018): 1641. http://dx.doi.org/10.3390/w10111641.
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In the last few decades tremendous progress has been made in the use of catchment models for the analysis and understanding of hydrologic systems. A common application involves the use of these models to predict flows at catchment outputs. However, the outputs predicted by these models are often deterministic because they focused only on the most probable forecast without an explicit estimate of the associated uncertainty. This paper uses Bayesian and Generalized Likelihood Uncertainty Estimation (GLUE) approaches to estimate uncertainty in catchment modelling parameter values and uncertainty in design flow estimates. Testing of join probability of both these estimates has been conducted for a monsoon catchment in Vietnam. The paper focuses on computational efficiency and the differences in results, regardless of the philosophies and mathematical rigor of both methods. It was found that the application of GLUE and Bayesian techniques resulted in parameter values that were statistically different. The design flood quantiles estimated by the GLUE method were less scattered than those resulting from the Bayesian approach when using a closer threshold value (1 standard deviation departed from the mean). More studies are required to evaluate the impact of threshold in GLUE on design flood estimation.
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Chen, Ziqi, Kirill V. Horoshenkov, and Ning Xiang. "Bayesian inference for boundary admittance estimation using a multipole model for room-acoustic simulation." Journal of the Acoustical Society of America 150, no. 4 (2021): A348. http://dx.doi.org/10.1121/10.0008540.
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Acoustic surface admittance/impedance at room boundaries is essential for wave-based room-acoustic simulations. In this work, two levels of Bayesian inference are applied to estimate the surface admittance based on a multipole admittance model. This work estimates the order of the multipole admittance model through the high level of inference, Bayesian model selection. The first (low) level of inference, Bayesian parameter estimation, is applied to estimate the parameter values of the surface admittance model once model order is selected. This work approximates the frequency-dependent admittance from experimentally measured a set of acoustic surface admittance data. Analysis results demonstrate that multipole model-based Bayesian inference is well suited in estimating the frequency-dependent boundary condition within wave-based simulation framework. Numerical simulations verify the estimation results of Bayesian inference.
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Vilar, M. J., J. Ranta, S. Virtanen, and H. Korkeala. "Bayesian Estimation of the True Prevalence and of the Diagnostic Test Sensitivity and Specificity of EnteropathogenicYersiniain Finnish Pig Serum Samples." BioMed Research International 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/931542.
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Bayesian analysis was used to estimate the pig’s and herd’s true prevalence of enteropathogenicYersiniain serum samples collected from Finnish pig farms. The sensitivity and specificity of the diagnostic test were also estimated for the commercially available ELISA which is used for antibody detection against enteropathogenicYersinia. The Bayesian analysis was performed in two steps; the first step estimated the prior true prevalence of enteropathogenicYersiniawith data obtained from a systematic review of the literature. In the second step, data of the apparent prevalence (cross-sectional study data), prior true prevalence (first step), and estimated sensitivity and specificity of the diagnostic methods were used for building the Bayesian model. The true prevalence ofYersiniain slaughter-age pigs was 67.5% (95% PI 63.2–70.9). The true prevalence ofYersiniain sows was 74.0% (95% PI 57.3–82.4). The estimates of sensitivity and specificity values of the ELISA were 79.5% and 96.9%.
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Rabie, Abdalla, and Junping Li. "E-Bayesian Estimation Based on Burr-X Generalized Type-II Hybrid Censored Data." Symmetry 11, no. 5 (2019): 626. http://dx.doi.org/10.3390/sym11050626.
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In this article, we are concerned with the E-Bayesian (the expectation of Bayesian estimate) method, the maximum likelihood and the Bayesian estimation methods of the shape parameter, and the reliability function of one-parameter Burr-X distribution. A hybrid generalized Type-II censored sample from one-parameter Burr-X distribution is considered. The Bayesian and E-Bayesian approaches are studied under squared error and LINEX loss functions by using the Markov chain Monte Carlo method. Confidence intervals for maximum likelihood estimates, as well as credible intervals for the E-Bayesian and Bayesian estimates, are constructed. Furthermore, an example of real-life data is presented for the sake of the illustration. Finally, the performance of the E-Bayesian estimation method is studied then compared with the performance of the Bayesian and maximum likelihood methods.
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Muharisa, Catrin, Ferra Yanuar, and Dodi Devianto. "Simulation Study The Using of Bayesian Quantile Regression in Nonnormal Error." CAUCHY 5, no. 3 (2018): 121. http://dx.doi.org/10.18860/ca.v5i3.5633.
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The purposes of this paper is to introduce the ability of the Bayesian quantile regression method in overcoming the problem of the nonnormal errors using asymmetric laplace distribution on simulation study. <strong>Method: </strong>We generate data and set distribution of error is asymmetric laplace distribution error, which is non normal data. In this research, we solve the nonnormal problem using quantile regression method and Bayesian quantile regression method and then we compare. The approach of the quantile regression is to separate or divide the data into any quantiles, estimate the conditional quantile function and minimize absolute error that is asymmetrical. Bayesian regression method used the asymmetric laplace distribution in likelihood function. Markov Chain Monte Carlo method using Gibbs sampling algorithm is applied then to estimate the parameter in Bayesian regression method. Convergency and confidence interval of parameter estimated are also checked. <strong>Result: </strong>Bayesian quantile regression method results has more significance parameter and smaller confidence interval than quantile regression method. <strong>Conclusion: </strong>This study proves that Bayesian quantile regression method can produce acceptable parameter estimate for nonnormal error.
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Chen, Lu, Daping Bi, and Jifei Pan. "Two-Dimensional Angle Estimation of Two-Parallel Nested Arrays Based on Sparse Bayesian Estimation." Sensors 18, no. 10 (2018): 3553. http://dx.doi.org/10.3390/s18103553.
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To increase the number of estimable signal sources, two-parallel nested arrays are proposed, which consist of two subarrays with sensors, and can estimate the two-dimensional (2-D) direction of arrival (DOA) of signal sources. To solve the problem of direction finding with two-parallel nested arrays, a 2-D DOA estimation algorithm based on sparse Bayesian estimation is proposed. Through a vectorization matrix, smoothing reconstruction matrix and singular value decomposition (SVD), the algorithm reduces the size of the sparse dictionary and data noise. A sparse Bayesian learning algorithm is used to estimate one dimension angle. By a joint covariance matrix, another dimension angle is estimated, and the estimated angles from two dimensions can be automatically paired. The simulation results show that the number of DOA signals that can be estimated by the proposed two-parallel nested arrays is much larger than the number of sensors. The proposed two-dimensional DOA estimation algorithm has excellent estimation performance.
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Staggs, Vincent S., and Byron J. Gajewski. "Bayesian and frequentist approaches to assessing reliability and precision of health-care provider quality measures." Statistical Methods in Medical Research 26, no. 3 (2015): 1341–49. http://dx.doi.org/10.1177/0962280215577410.
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Our purpose was to compare frequentist, empirical Bayes, and Bayesian hierarchical model approaches to estimating reliability of health care quality measures, including construction of credible intervals to quantify uncertainty in reliability estimates, using data on inpatient fall rates on hospital nursing units. Precision of reliability estimates and Bayesian approaches to estimating reliability are not well studied. We analyzed falls data from 2372 medical units; the rate of unassisted falls per 1000 inpatient days was the measure of interest. The Bayesian methods “shrunk” the observed fall rates and frequentist reliability estimates toward their posterior means. We examined the association between reliability and precision in fall rate rankings by plotting the length of a 90% credible interval for each unit’s percentile rank against the unit’s estimated reliability. Precision of rank estimates tended to increase as reliability increased but was limited even at higher reliability levels: Among units with reliability >0.8, only 5.5% had credible interval length <20; among units with reliability >0.9, only 31.9% had credible interval length <20. Thus, a high reliability estimate may not be sufficient to ensure precise differentiation among providers. Bayesian approaches allow for assessment of this precision.
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Caraiani, P. "Bayesian estimation of the Okun coefficient for Romania." Acta Oeconomica 60, no. 1 (2010): 79–92. http://dx.doi.org/10.1556/aoecon.60.2010.1.5.
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In this paper I use a New Keynesian model with unemployment and estimate it for the Romanian economy using Bayesian techniques. I use the estimated model to derive an estimation of the Okun coefficient. I alternatively estimate the Okun coefficient using the Bayesian linear regression. The results show that the Okun coefficient is high in the Romanian economy implying that the current crisis will have a severe impact on the labour market as well as important social effects.
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Thorson, James T., and Jim Berkson. "Multispecies estimation of Bayesian priors for catchability trends and density dependence in the US Gulf of Mexico." Canadian Journal of Fisheries and Aquatic Sciences 67, no. 6 (2010): 936–54. http://dx.doi.org/10.1139/f10-040.
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Fishery-dependent catch-per-unit-effort (CPUE) derived indices of stock abundance are commonly used in fishery stock assessment models and may be significantly biased due to changes in catchability over time. Factors causing time-varying catchability include density-dependent habitat selection and technology improvements such as global positioning systems. In this study, we develop a novel multispecies method to estimate Bayesian priors for catchability functional parameters. This method uses the deviance information criterion to select a parsimonious functional model for catchability among 10 hierarchical and measurement error models. The parsimonious model is then applied to multispecies data, while excluding one species at a time, to develop Bayesian priors that can be used for each excluded species. We use this method to estimate catchability trends and density dependence for seven stocks and four gears in the Gulf of Mexico by comparing CPUE-derived index data with abundance estimates from virtual population analysis calibrated with fishery-independent indices. Catchability density dependence estimates mean that CPUE indices are hyperstable, implying that stock rebuilding in the Gulf may be progressing faster than previously estimated. This method for estimating Bayesian priors can provide a parsimonious method to compensate for time-varying catchability and uses multispecies fishery data in a novel manner.
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Dutta, Subhankar, Hana N. Alqifari, and Amani Almohaimeed. "Bayesian and non-bayesian inference for logistic-exponential distribution using improved adaptive type-II progressively censored data." PLOS ONE 19, no. 5 (2024): e0298638. http://dx.doi.org/10.1371/journal.pone.0298638.
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Improved adaptive type-II progressive censoring schemes (IAT-II PCS) are increasingly being used to estimate parameters and reliability characteristics of lifetime distributions, leading to more accurate and reliable estimates. The logistic exponential distribution (LED), a flexible distribution with five hazard rate forms, is employed in several fields, including lifetime, financial, and environmental data. This research aims to enhance the accuracy and reliability estimation capabilities for the logistic exponential distribution under IAT-II PCS. By developing novel statistical inference methods, we can better understand the behavior of failure times, allow for more accurate decision-making, and improve the overall reliability of the model. In this research, we consider both classical and Bayesian techniques. The classical technique involves constructing maximum likelihood estimators of the model parameters and their asymptotic covariance matrix, followed by estimating the distribution’s reliability using survival and hazard functions. The delta approach is used to create estimated confidence intervals for the model parameters. In the Bayesian technique, prior information about the LED parameters is used to estimate the posterior distribution of the parameters, which is derived using Bayes’ theorem. The model’s reliability is determined by computing the posterior predictive distribution of the survival or hazard functions. Extensive simulation studies and real-data applications assess the effectiveness of the proposed methods and evaluate their performance against existing methods.
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Wang, Chenlan, Chongjie Zhang, and X. Jessie Yang. "Automation reliability and trust: A Bayesian inference approach." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 202–6. http://dx.doi.org/10.1177/1541931218621048.
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Research shows that over repeated interactions with automation, human operators are able to learn how reliable the automation is and update their trust in automation. The goal of the present study is to investigate if this learning and inference process approximately follow the principle of Bayesian probabilistic inference. First, we applied Bayesian inference to estimate human operators’ perceived system reliability and found high correlations between the Bayesian estimates and the perceived reliability for the majority of the participants. We then correlated the Bayesian estimates with human operators’ reported trust and found moderate correlations for a large portion of the participants. Our results suggest that human operators’ learning and inference process for automation reliability can be approximated by Bayesian inference.
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Vallejo, Benjamin, Jr, and Alexander Aloy. "Estimating the rarity of birds and its ecological context in the University of the Philippines Diliman Campus." SciEnggJ 1, no. 1 (2008): 32–36. http://dx.doi.org/10.54645/ipyz58175.
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In this paper we use frequentist (classical) and Bayesian inference to estimate the rarity of birds in the University of the Philippines Diliman campus. Rare species have a detection probability of 1%. As sightings of certain species of birds are extremely rare, a frequentist approach to estimation will often result in overestimates of observation precision. Using McArdle’s rarity and Bayesian inference we estimated the probability of detecting rare species as between 4 to 10%. The Bayesian estimates are lower and may be a better method for estimating rarity. The coefficient of variation (CV) of the frequentist (24%) and Bayesian (25%) estimates are similar suggesting imprecision. Rare species are likely to be detected in areas of campus with common and highly abundant species. This may be an artifact of the fast paced Jokimäki method that was used in surveys of bird abundances. The higher probability of detecting rare species from the suggested 1% cutoff and similar CV estimates are likely due to the small data set used in the estimations and the lack of prior information. The ecological context of our observations is related to the increasing fragmentation of habitat in the campus as a consequence of urbanization.
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McCann, Brian T. "Using Bayesian Updating to Improve Decisions under Uncertainty." California Management Review 63, no. 1 (2020): 26–40. http://dx.doi.org/10.1177/0008125620948264.
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Decision making requires managers to constantly estimate the probability of uncertain outcomes and update those estimates in light of new information. This article provides guidance to managers on how they can improve that process by more explicitly adopting a Bayesian approach. Clear understanding and application of the Bayesian approach leads to more accurate probability estimates, resulting in better informed decisions. More importantly, adopting a Bayesian approach, even informally, promises to improve the quality of managerial thinking, analysis, and decisions in a variety of additional ways.
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Nagy, M., M. H. Abu-Moussa, Adel Fahad Alrasheedi, and A. Rabie. "Expected Bayesian estimation for exponential model based on simple step stress with Type-I hybrid censored data." Mathematical Biosciences and Engineering 19, no. 10 (2022): 9773–91. http://dx.doi.org/10.3934/mbe.2022455.
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<abstract><p>The procedure of selecting the values of hyper-parameters for prior distributions in Bayesian estimate has produced many problems and has drawn the attention of many authors, therefore the expected Bayesian (E-Bayesian) estimation method to overcome these problems. These approaches are used based on the step-stress acceleration model under the Exponential Type-I hybrid censored data in this study. The values of the distribution parameters are derived. To compare the E-Bayesian estimates to the other estimates, a comparative study was conducted using the simulation research. Four different loss functions are used to generate the Bayesian and E-Bayesian estimators. In addition, three alternative hyper-parameter distributions were used in E-Bayesian estimation. Finally, a real-world data example is examined for demonstration and comparative purposes.</p></abstract>
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Oladyshkin, Sergey, and Wolfgang Nowak. "The Connection between Bayesian Inference and Information Theory for Model Selection, Information Gain and Experimental Design." Entropy 21, no. 11 (2019): 1081. http://dx.doi.org/10.3390/e21111081.
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We show a link between Bayesian inference and information theory that is useful for model selection, assessment of information entropy and experimental design. We align Bayesian model evidence (BME) with relative entropy and cross entropy in order to simplify computations using prior-based (Monte Carlo) or posterior-based (Markov chain Monte Carlo) BME estimates. On the one hand, we demonstrate how Bayesian model selection can profit from information theory to estimate BME values via posterior-based techniques. Hence, we use various assumptions including relations to several information criteria. On the other hand, we demonstrate how relative entropy can profit from BME to assess information entropy during Bayesian updating and to assess utility in Bayesian experimental design. Specifically, we emphasize that relative entropy can be computed avoiding unnecessary multidimensional integration from both prior and posterior-based sampling techniques. Prior-based computation does not require any assumptions, however posterior-based estimates require at least one assumption. We illustrate the performance of the discussed estimates of BME, information entropy and experiment utility using a transparent, non-linear example. The multivariate Gaussian posterior estimate includes least assumptions and shows the best performance for BME estimation, information entropy and experiment utility from posterior-based sampling.
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Jackman, Simon. "Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation." Political Analysis 8, no. 4 (2000): 307–32. http://dx.doi.org/10.1093/oxfordjournals.pan.a029818.
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Bayesian simulation is increasingly exploited in the social sciences for estimation and inference of model parameters. But an especially useful (if often overlooked) feature of Bayesian simulation is that it can be used to estimate any function of model parameters, including “auxiliary” quantities such as goodness-of-fit statistics, predicted values, and residuals. Bayesian simulation treats these quantities as if they were missing data, sampling from their implied posterior densities. Exploiting this principle also lets researchers estimate models via Bayesian simulation where maximum-likelihood estimation would be intractable. Bayesian simulation thus provides a unified solution for quantitative social science. I elaborate these ideas in a variety of contexts: these include generalized linear models for binary responses using data on bill cosponsorship recently reanalyzed in Political Analysis, item—response models for the measurement of respondent's levels of political information in public opinion surveys, the estimation and analysis of legislators' ideal points from roll-call data, and outlier-resistant regression estimates of incumbency advantage in U.S. Congressional elections
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Jamnia, Abdul Rashid, Ahmad Ali Keikha, Mahmoud Ahmadpour, Abdoul Ahad Cissé, and Mohammad Rokouei. "Applying bayesian population assessment models to artisanal, multispecies fisheries in the Northern Mokran Sea, Iran." Nature Conservation 28 (August 13, 2018): 61–89. http://dx.doi.org/10.3897/natureconservation.28.25212.
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Small-scale fisheries substantially contribute to the reduction of poverty, local economies and food safety in many countries. However, limited and low-quality catches and effort data for small-scale fisheries complicate the stock assessment and management. Bayesian modelling has been advocated when assessing fisheries with limited data. Specifically, Bayesian models can incorporate information of the multiple sources, improve precision in the stock assessments and provide specific levels of uncertainty for estimating the relevant parameters. In this study, therefore, the state-space Bayesian generalised surplus production models will be used in order to estimate the stock status of fourteen Demersal fish species targeted by small-scale fisheries in Sistan and Baluchestan, Iran. The model was estimated using Markov chain Monte Carlo (MCMC) and Gibbs Sampling. Model parameter estimates were evaluated by the formal convergence and stationarity diagnostic tests, indicating convergence and accuracy. They were also aligned with existing parameter estimates for fourteen species of the other locations. This suggests model reliability and demonstrates the utility of Bayesian models. According to estimated fisheries’ management reference points, all assessed fish stocks appear to be overfished. Overfishing considered, the current fisheries management strategies for the small-scale fisheries may need some adjustments to warrant the long-term viability of the fisheries.
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Jamnia, Abdul Rashid, Ahmad Ali Keikha, Mahmoud Ahmadpour, Abdoul Ahad Cissé, and Mohammad Rokouei. "Applying bayesian population assessment models to artisanal, multispecies fisheries in the Northern Mokran Sea, Iran." Nature Conservation 28 (August 13, 2018): 61–89. https://doi.org/10.3897/natureconservation.28.25212.
Full textAbstract:
Small-scale fisheries substantially contribute to the reduction of poverty, local economies and food safety in many countries. However, limited and low-quality catches and effort data for small-scale fisheries complicate the stock assessment and management. Bayesian modelling has been advocated when assessing fisheries with limited data. Specifically, Bayesian models can incorporate information of the multiple sources, improve precision in the stock assessments and provide specific levels of uncertainty for estimating the relevant parameters. In this study, therefore, the state-space Bayesian generalised surplus production models will be used in order to estimate the stock status of fourteen Demersal fish species targeted by small-scale fisheries in Sistan and Baluchestan, Iran. The model was estimated using Markov chain Monte Carlo (MCMC) and Gibbs Sampling. Model parameter estimates were evaluated by the formal convergence and stationarity diagnostic tests, indicating convergence and accuracy. They were also aligned with existing parameter estimates for fourteen species of the other locations. This suggests model reliability and demonstrates the utility of Bayesian models. According to estimated fisheries' management reference points, all assessed fish stocks appear to be overfished. Overfishing considered, the current fisheries management strategies for the small-scale fisheries may need some adjustments to warrant the long-term viability of the fisheries.
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Smith, Jordan W., Lindsey S. Smart, Monica A. Dorning, Lauren Nicole Dupéy, Andréanne Méley, and Ross K. Meentemeyer. "Bayesian methods to estimate urban growth potential." Landscape and Urban Planning 163 (July 2017): 1–16. http://dx.doi.org/10.1016/j.landurbplan.2017.03.004.
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Sparacino, Giovanni, Stefano Milani, Edoardo Arslan, and Claudio Cobelli. "A Bayesian approach to estimate evoked potentials." Computer Methods and Programs in Biomedicine 68, no. 3 (2002): 233–48. http://dx.doi.org/10.1016/s0169-2607(01)00175-4.
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Han, Ming, and Yuanyao Ding. "Synthesized expected Bayesian method of parametric estimate." Journal of Systems Science and Systems Engineering 13, no. 1 (2004): 98–111. http://dx.doi.org/10.1007/s11518-006-0156-0.
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Riedel, Michael, Stan E. Dosso, and Laurens Beran. "Uncertainty estimation for amplitude variation with offset (AVO) inversion." GEOPHYSICS 68, no. 5 (2003): 1485–96. http://dx.doi.org/10.1190/1.1620621.
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This paper uses a Bayesian approach for inverting seismic amplitude versus offset (AVO) data to provide estimates and uncertainties of the viscoelastic physical parameters at an interface. The inversion is based on Gibbs' sampling approach to determine properties of the posterior probability distribution (PPD), such as the posterior mean, maximum a posteriori (MAP) estimate, marginal probability distributions, and covariances. The Bayesian formulation represents a fully nonlinear inversion; the results are compared to those of standard linearized inversion. The nonlinear and linearized approaches are applied to synthetic test cases which consider AVO inversion for shallow marine environments with both unconsolidated and consolidated seabeds. The result of neglecting attenuation in the seabed is investigated, and the effects of data factors such as independent and systematic errors and the range of incident angles are considered. The Bayesian approach is also applied to estimate the physical parameters and uncertainties from AVO data collected at two sites along a seismic line in the Baltic Sea with differing sediment types; it clearly identifies the distinct seabed compositions. Data uncertainties (independent and systematic) required for this analysis are estimated using a maximum‐likelihood approach.
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Kawahara, Daisuke, and Shigeyoshi Fujisawa. "Advantages of Persistent Cohomology in Estimating Animal Location From Grid Cell Population Activity." Neural Computation 36, no. 3 (2024): 385–411. http://dx.doi.org/10.1162/neco_a_01645.
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Abstract Many cognitive functions are represented as cell assemblies. In the case of spatial navigation, the population activity of place cells in the hippocampus and grid cells in the entorhinal cortex represents self-location in the environment. The brain cannot directly observe self-location information in the environment. Instead, it relies on sensory information and memory to estimate self-location. Therefore, estimating low-dimensional dynamics, such as the movement trajectory of an animal exploring its environment, from only the high-dimensional neural activity is important in deciphering the information represented in the brain. Most previous studies have estimated the low-dimensional dynamics (i.e., latent variables) behind neural activity by unsupervised learning with Bayesian population decoding using artificial neural networks or gaussian processes. Recently, persistent cohomology has been used to estimate latent variables from the phase information (i.e., circular coordinates) of manifolds created by neural activity. However, the advantages of persistent cohomology over Bayesian population decoding are not well understood. We compared persistent cohomology and Bayesian population decoding in estimating the animal location from simulated and actual grid cell population activity. We found that persistent cohomology can estimate the animal location with fewer neurons than Bayesian population decoding and robustly estimate the animal location from actual noisy data.
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de Lima, Max Sousa, and Gregorio Saravia Atuncar. "A Bayesian method to estimate the optimal bandwidth for multivariate kernel estimator." Journal of Nonparametric Statistics 23, no. 1 (2011): 137–48. http://dx.doi.org/10.1080/10485252.2010.485200.
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