Статті в журналах: "Bayesian therory"

1

Thomson, A. H. "BAYESIAN FEEDBACK METHODS FOR OPTIMISING THERAPY." Clinical Neuropharmacology 15 (1992): 245A—246A. http://dx.doi.org/10.1097/00002826-199201001-00128.

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Abrams, Keith, Deborah Ashby, and Doug Errington. "Bayesian analysis of neutron therapy trial." Controlled Clinical Trials 12, no. 5 (October 1991): 666–67. http://dx.doi.org/10.1016/0197-2456(91)90200-6.

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3

Duan, Leo L., Alexander L. Young, Akihiko Nishimura, and David B. Dunson. "Bayesian constraint relaxation." Biometrika 107, no. 1 (December 24, 2019): 191–204. http://dx.doi.org/10.1093/biomet/asz069.

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Summary Prior information often takes the form of parameter constraints. Bayesian methods include such information through prior distributions having constrained support. By using posterior sampling algorithms, one can quantify uncertainty without relying on asymptotic approximations. However, sharply constrained priors are not necessary in some settings and tend to limit modelling scope to a narrow set of distributions that are tractable computationally. We propose to replace the sharp indicator function of the constraint with an exponential kernel, thereby creating a close-to-constrained neighbourhood within the Euclidean space in which the constrained subspace is embedded. This kernel decays with distance from the constrained space at a rate depending on a relaxation hyperparameter. By avoiding the sharp constraint, we enable use of off-the-shelf posterior sampling algorithms, such as Hamiltonian Monte Carlo, facilitating automatic computation in a broad range of models. We study the constrained and relaxed distributions under multiple settings and theoretically quantify their differences. Application of the method is illustrated through several novel modelling examples.

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Neoptolemos, John P., and Trevor F. Cox. "Bayesian analysis unravels pancreas-cancer adjuvant therapy." Lancet Oncology 14, no. 11 (October 2013): 1034–35. http://dx.doi.org/10.1016/s1470-2045(13)70403-0.

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5

Tidwell, Rebecca S. Slack, S. Andrew Peng, Minxing Chen, Diane D. Liu, Ying Yuan, and J. Jack Lee. "Bayesian clinical trials at The University of Texas MD Anderson Cancer Center: An update." Clinical Trials 16, no. 6 (August 26, 2019): 645–56. http://dx.doi.org/10.1177/1740774519871471.

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Background/aims In our 2009 article, we showed that Bayesian methods had established a foothold in developing therapies in our institutional oncology trials. In this article, we will document what has happened since that time. In addition, we will describe barriers to implementing Bayesian clinical trials, as well as our experience overcoming them. Methods We reviewed MD Anderson Cancer Center clinical trials submitted to the institutional protocol office for scientific and ethical review between January 2009 and December 2013, the same length time period as the previous article. We tabulated Bayesian methods implemented for design or analyses for each trial and then compared these to our previous findings. Results Overall, we identified 1020 trials and found that 283 (28%) had Bayesian components so we designated them as Bayesian trials. Among MD Anderson–only and multicenter trials, 56% and 14%, respectively, were Bayesian, higher rates than our previous study. Bayesian trials were more common in phase I/II trials (34%) than in phase III/IV (6%) trials. Among Bayesian trials, the most commonly used features were for toxicity monitoring (65%), efficacy monitoring (36%), and dose finding (22%). The majority (86%) of Bayesian trials used non-informative priors. A total of 75 (27%) trials applied Bayesian methods for trial design and primary endpoint analysis. Among this latter group, the most commonly used methods were the Bayesian logistic regression model (N = 22), the continual reassessment method (N = 20), and adaptive randomization (N = 16). Median institutional review board approval time from protocol submission was the same 1.4 months for Bayesian and non-Bayesian trials. Since the previous publication, the Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial was the first large-scale decision trial combining multiple treatments in a single trial. Since then, two regimens in breast cancer therapy have been identified and published from the cooperative Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis (I-SPY 2), enhancing cooperation among investigators and drug developers across the nation, as well as advancing information needed for personalized medicine. Many software programs and Shiny applications for Bayesian trial design and calculations are available from our website which has had more than 21,000 downloads worldwide since 2004. Conclusion Bayesian trials have the increased flexibility in trial design needed for personalized medicine, resulting in more cooperation among researchers working to fight against cancer. Some disadvantages of Bayesian trials remain, but new methods and software are available to improve their function and incorporation into cancer clinical research.

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Büchter, Theresa, Nicole Steib, Katharina Böcherer-Linder, Andreas Eichler, Stefan Krauss, Karin Binder, and Markus Vogel. "Designing Visualisations for Bayesian Problems According to Multimedia Principles." Education Sciences 12, no. 11 (October 25, 2022): 739. http://dx.doi.org/10.3390/educsci12110739.

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Questions involving Bayesian Reasoning often arise in events of everyday life, such as assessing the results of a breathalyser test or a medical diagnostic test. Bayesian Reasoning is perceived to be difficult, but visualisations are known to support it. However, prior research on visualisations for Bayesian Reasoning has only rarely addressed the issue on how to design such visualisations in the most effective way according to research on multimedia learning. In this article, we present a concise overview on subject-didactical considerations, together with the most fundamental research of both Bayesian Reasoning and multimedia learning. Building on these aspects, we provide a step-by-step development of the design of visualisations which support Bayesian problems, particularly for so-called double-trees and unit squares.

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Mukha, V. S. "Comparative numerical analysis of Bayesian decision rule and probabilistic neural network for pattern recognition." Doklady BGUIR 19, no. 7 (November 25, 2021): 13–21. http://dx.doi.org/10.35596/1729-7648-2021-19-7-13-21.

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At present, neural networks are increasingly used to solve many problems instead of traditional methods for solving them. This involves comparing the neural network and the traditional method for specific tasks. In this paper, computer modeling of the Bayesian decision rule and the probabilistic neural network is carried out in order to compare their operational characteristics for recognizing Gaussian patterns. Recognition of four and six images (classes) with the number of features from 1 to 6 was simulated in cases where the images are well and poorly separated. The sizes of the training and test samples are chosen quiet big: 500 implementations for each image. Such characteristics as training time of the decision rule, recognition time on the test sample, recognition reliability on the test sample, recognition reliability on the training sample were analyzed. In framework of these conditions it was found that the recognition reliability on the test sample in the case of well separated patterns and with any number of the instances is close to 100 percent for both decision rules. The neural network loses 0,1–16 percent to Bayesian decision rule in the recognition reliability on the test sample for poorly separated patterns. The training time of the neural network exceeds the training time of the Bayesian decision rule in 4–5 times and the recognition time – in 4–6 times. As a result, there are no obvious advantages of the probabilistic neural network over the Bayesian decision rule in the problem of Gaussian pattern recognition. The existing generalization of the Bayesian decision rule described in the article is an alternative to the neural network for the case of non-Gaussian patterns.

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Quintela-del-Río, Alejandro, Beatriz Rodríguez-Romero, Verónica Robles-García, Pablo Arias-Rodríguez, Javier Cudeiro-Mazaira, and Alicia Martínez-Rodríguez. "Bayesian Methods in the Field of Rehabilitation." American Journal of Physical Medicine & Rehabilitation 98, no. 6 (June 2019): 516–20. http://dx.doi.org/10.1097/phm.0000000000001124.

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McAleer, S. D., H. Chrystyn, and D. A. G. Newton. "BAYESIAN DERIVED PHARMACOKINETIC/PHARMACODYNAMIC VARIABLES FROM WARFARIN THERAPY." Journal of Pharmacy and Pharmacology 42, S1 (December 1990): 189P. http://dx.doi.org/10.1111/j.2042-7158.1990.tb14562.x.

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Cao, Ming, Yue Fan, and Qinke Peng. "Bayesian Gene Selection Based on Pathway Information and Network-Constrained Regularization." Computational and Mathematical Methods in Medicine 2021 (August 4, 2021): 1–9. http://dx.doi.org/10.1155/2021/7471516.

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High-throughput data make it possible to study expression levels of thousands of genes simultaneously under a particular condition. However, only few of the genes are discriminatively expressed. How to identify these biomarkers precisely is significant for disease diagnosis, prognosis, and therapy. Many studies utilized pathway information to identify the biomarkers. However, most of these studies only incorporate the group information while the pathway structural information is ignored. In this paper, we proposed a Bayesian gene selection with a network-constrained regularization method, which can incorporate the pathway structural information as priors to perform gene selection. All the priors are conjugated; thus, the parameters can be estimated effectively through Gibbs sampling. We present the application of our method on 6 microarray datasets, comparing with Bayesian Lasso, Bayesian Elastic Net, and Bayesian Fused Lasso. The results show that our method performs better than other Bayesian methods and pathway structural information can improve the result.

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Shadmehr, Reza, and David Z. D'Argenio. "A Neural Network for Nonlinear Bayesian Estimation in Drug Therapy." Neural Computation 2, no. 2 (June 1990): 216–25. http://dx.doi.org/10.1162/neco.1990.2.2.216.

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The feasibility of developing a neural network to perform nonlinear Bayesian estimation from sparse data is explored using an example from clinical pharmacology. The problem involves estimating parameters of a dynamic model describing the pharmacokinetics of the bronchodilator theophylline from limited plasma concentration measurements of the drug obtained in a patient. The estimation performance of a backpropagation trained network is compared to that of the maximum likelihood estimator as well as the maximum a posteriori probability estimator. In the example considered, the estimator prediction errors (model parameters and outputs) obtained from the trained neural network were similar to those obtained using the nonlinear Bayesian estimator.

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Ebert, Philip A. "Bayesian reasoning in avalanche terrain: a theoretical investigation." Journal of Adventure Education and Outdoor Learning 19, no. 1 (August 22, 2018): 84–95. http://dx.doi.org/10.1080/14729679.2018.1508356.

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13

Safiloo, Samad, Yadollah Mehrabi, Sareh Asadi, and Soheila Khodakarim. "Response to Fluvoxamine in the Obsessive-Compulsive Disorder Patients: Bayesian Ordinal Quantile Regression." Clinical Practice & Epidemiology in Mental Health 17, no. 1 (October 15, 2021): 146–51. http://dx.doi.org/10.2174/1745017902117010151.

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Background: Obsessive-Compulsive Disorder (OCD) is a chronic neuropsychiatric disorder associated with unpleasant thoughts or mental images, making the patient repeat physical or mental behaviors to relieve discomfort. 40-60% of patients do not respond to Serotonin Reuptake Inhibitors, including fluvoxamine therapy. Introduction: The aim of the study is to identify the predictors of fluvoxamine therapy in OCD patients by Bayesian Ordinal Quantile Regression Model. Methods: This study was performed on 109 patients with OCD. Three methods, including Bayesian ordinal quantile, probit, and logistic regression models, were applied to identify predictors of response to fluvoxamine. The accuracy and weighted kappa were used to evaluate these models. Results: Our result showed that rs3780413 (mean=-0.69, sd=0.39) and cleaning dimension (mean=-0.61, sd=0.20) had reverse effects on response to fluvoxamine therapy in Bayesian ordinal probit and logistic regression models. In the 75th quantile regression model, marital status (mean=1.62, sd=0.47) and family history (mean=1.33, sd=0.61) had a direct effect, and cleaning (mean=-1.10, sd=0.37) and somatic (mean=-0.58, sd=0.27) dimensions had reverse effects on response to fluvoxamine therapy. Conclusion: Response to fluvoxamine is a multifactorial problem and can be different in the levels of socio-demographic, genetic, and clinical predictors. Marital status, familial history, cleaning, and somatic dimensions are associated with response to fluvoxamine therapy.

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Leehey, D. J., B. I. Braun, D. A. Tholl, L. S. Chung, C. A. Gross, J. A. Roback, and J. R. Lentino. "Can pharmacokinetic dosing decrease nephrotoxicity associated with aminoglycoside therapy." Journal of the American Society of Nephrology 4, no. 1 (July 1993): 81–90. http://dx.doi.org/10.1681/asn.v4181.

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A randomized, controlled clinical trial was performed to determine whether individualized dosing by use of Bayesian pharmacokinetic modeling could decrease nephrotoxicity accosted with aminoglycoside therapy. Two hundred forty-three patients receiving aminoglycosides for suspected or proven infection were randomly assigned to one of three groups: usual physician-directed dosing (Group 1), pharmacist-assisted dosing (Group 2), or pharmacist-directed dosing (Group 3). Dosing in Groups 2 and 3 was based on a Bayesian pharmacokinetic dosing program, whereas Group 1 served as the control group. Individualized dosing resulted in higher mean postinfusion (peak) serum aminoglycoside levels, higher ratios of mean peak level to minimum inhibitory concentration (peak/MIC ratios), and a trend toward lower trough serum levels. Milligrams per dose were higher and number of doses per day was lower in the pharmacist-dosed groups. However, the incidence of nephrotoxicity (> or = 100% increase in serum creatinine) was not different among the three groups (16, 27, and 16% in Groups 1, 2, and 3, respectively). Similarly, severity of toxicity was not affected by the dosing intervention. Risk factors for toxicity included duration of therapy, shock, treatment with furosemide, older age, and liver disease. After controlling for these factors, the dosing intervention still had no effect on nephrotoxicity. It was concluded that Bayesian pharmacokinetic dosing did not decrease the risk of nephrotoxicity associated with aminoglycoside therapy.

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Chu, Yiyi, and Ying Yuan. "A Bayesian basket trial design using a calibrated Bayesian hierarchical model." Clinical Trials 15, no. 2 (March 2, 2018): 149–58. http://dx.doi.org/10.1177/1740774518755122.

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Background: The basket trial evaluates the treatment effect of a targeted therapy in patients with the same genetic or molecular aberration, regardless of their cancer types. Bayesian hierarchical modeling has been proposed to adaptively borrow information across cancer types to improve the statistical power of basket trials. Although conceptually attractive, research has shown that Bayesian hierarchical models cannot appropriately determine the degree of information borrowing and may lead to substantially inflated type I error rates. Methods: We propose a novel calibrated Bayesian hierarchical model approach to evaluate the treatment effect in basket trials. In our approach, the shrinkage parameter that controls information borrowing is not regarded as an unknown parameter. Instead, it is defined as a function of a similarity measure of the treatment effect across tumor subgroups. The key is that the function is calibrated using simulation such that information is strongly borrowed across subgroups if their treatment effects are similar and barely borrowed if the treatment effects are heterogeneous. Results: The simulation study shows that our method has substantially better controlled type I error rates than the Bayesian hierarchical model. In some scenarios, for example, when the true response rate is between the null and alternative, the type I error rate of the proposed method can be inflated from 10% up to 20%, but is still better than that of the Bayesian hierarchical model. Limitation: The proposed design assumes a binary endpoint. Extension of the proposed design to ordinal and time-to-event endpoints is worthy of further investigation. Conclusion: The calibrated Bayesian hierarchical model provides a practical approach to design basket trials with more flexibility and better controlled type I error rates than the Bayesian hierarchical model. The software for implementing the proposed design is available at http://odin.mdacc.tmc.edu/~yyuan/index_code.html

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Wei, Yanling, Wen Li, Jiyong Tan, Jianhui Yuan, Zhihui Wu, Yu Li, Yu’ang Mao, and Daizheng Huang. "A Method for Improving the Prediction of Outpatient Visits for Hospital Management: Bayesian Autoregressive Analysis." Computational and Mathematical Methods in Medicine 2022 (October 12, 2022): 1–15. http://dx.doi.org/10.1155/2022/4718157.

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The number of outpatient visits is generally influenced by various factors that are difficult to quantify and obtain, resulting in some irregular fluctuations. The traditional statistical methodology seldom considers these uncertainties. Accordingly, this paper presents a Bayesian autoregressive (AR) analysis to propose a forecasting framework to cope with the strict requirements. The AR model was conducted to identify the linear and autocorrelation relationships of historical series, and Bayesian inference was used to correct and optimize the AR model parameters. Posterior distribution of parameters was stably and reliably obtained by Gibbs sampling on the condition of the convergent Markov chain. Meanwhile, the lag orders of the AR model were adjusted based on the series characteristics. To increase the variability and generality of the dataset, the developed Bayesian AR model was evaluated at seven hospitals in China. The results demonstrated that the Bayesian AR model had varying degrees of decline in the MAPE value in the seven sets of experimental data. The reductions ranged from 0.1431% to 0.0342%, indicating effective optimization of the Bayesian inference in the AR model parameters and reflecting the useful correction of the lag order adjustment strategy. The proposed Bayesian AR framework showed high accuracy index and stable prediction accuracy, thereby outperforming the traditional AR model.

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Cao, Jack, Max Kleiman-Weiner, and Mahzarin R. Banaji. "People Make the Same Bayesian Judgment They Criticize in Others." Psychological Science 30, no. 1 (November 12, 2018): 20–31. http://dx.doi.org/10.1177/0956797618805750.

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When two individuals from different social groups exhibit identical behavior, egalitarian codes of conduct call for equal judgments of both individuals. However, this moral imperative is at odds with the statistical imperative to consider priors based on group membership. Insofar as these priors differ, Bayesian rationality calls for unequal judgments of both individuals. We show that participants criticized the morality and intellect of someone else who made a Bayesian judgment, shared less money with this person, and incurred financial costs to punish this person. However, participants made unequal judgments as a Bayesian statistician would, thereby rendering the same judgment that they found repugnant when offered by someone else. This inconsistency, which can be reconciled by differences in which base rate is attended to, suggests that participants use group membership in a way that reflects the savvy of a Bayesian and the disrepute of someone they consider to be a bigot.

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Douven, Igor. "Decision theory and the rationality of further deliberation." Economics and Philosophy 18, no. 2 (October 2002): 303–28. http://dx.doi.org/10.1017/s0266267102002079.

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Bayesian decision theory operates under the fiction that in any decision-making situation the agent is simply given the options from which he is to choose. It thereby sets aside some characteristics of the decision-making situation that are pre-analytically of vital concern to the verdict on the agent's eventual decision. In this paper it is shown that and how these characteristics can be accommodated within a still recognizably Bayesian account of rational agency.

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Körding, Konrad P., Shih-pi Ku, and Daniel M. Wolpert. "Bayesian Integration in Force Estimation." Journal of Neurophysiology 92, no. 5 (November 2004): 3161–65. http://dx.doi.org/10.1152/jn.00275.2004.

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When we interact with objects in the world, the forces we exert are finely tuned to the dynamics of the situation. As our sensors do not provide perfect knowledge about the environment, a key problem is how to estimate the appropriate forces. Two sources of information can be used to generate such an estimate: sensory inputs about the object and knowledge about previously experienced objects, termed prior information. Bayesian integration defines the way in which these two sources of information should be combined to produce an optimal estimate. To investigate whether subjects use such a strategy in force estimation, we designed a novel sensorimotor estimation task. We controlled the distribution of forces experienced over the course of an experiment thereby defining the prior. We show that subjects integrate sensory information with their prior experience to generate an estimate. Moreover, subjects could learn different prior distributions. These results suggest that the CNS uses Bayesian models when estimating force requirements.

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Nguyen, Tuan V. "Pharmacogenetics of anti-resorptive therapy efficacy: a Bayesian interpretation." Osteoporosis International 16, no. 8 (February 1, 2005): 857–60. http://dx.doi.org/10.1007/s00198-004-1807-y.

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Höge, Marvin, Anneli Guthke, and Wolfgang Nowak. "Bayesian Model Weighting: The Many Faces of Model Averaging." Water 12, no. 2 (January 21, 2020): 309. http://dx.doi.org/10.3390/w12020309.

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Model averaging makes it possible to use multiple models for one modelling task, like predicting a certain quantity of interest. Several Bayesian approaches exist that all yield a weighted average of predictive distributions. However, often, they are not properly applied which can lead to false conclusions. In this study, we focus on Bayesian Model Selection (BMS) and Averaging (BMA), Pseudo-BMS/BMA and Bayesian Stacking. We want to foster their proper use by, first, clarifying their theoretical background and, second, contrasting their behaviours in an applied groundwater modelling task. We show that only Bayesian Stacking has the goal of model averaging for improved predictions by model combination. The other approaches pursue the quest of finding a single best model as the ultimate goal, and use model averaging only as a preliminary stage to prevent rash model choice. Improved predictions are thereby not guaranteed. In accordance with so-called M -settings that clarify the alleged relations between models and truth, we elicit which method is most promising.

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Yuille, Alan L., Stelios M. Smirnakis, and Lei Xu. "Bayesian Self-Organization Driven by Prior Probability Distributions." Neural Computation 7, no. 3 (May 1995): 580–95. http://dx.doi.org/10.1162/neco.1995.7.3.580.

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Recent work by Becker and Hinton (1992) shows a promising mechanism, based on maximizing mutual information assuming spatial coherence, by which a system can self-organize to learn visual abilities such as binocular stereo. We introduce a more general criterion, based on Bayesian probability theory, and thereby demonstrate a connection to Bayesian theories of visual perception and to other organization principles for early vision (Atick and Redlich 1990). Methods for implementation using variants of stochastic learning are described.

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McMillan, Garnett P., and John B. Cannon. "Bayesian Applications in Auditory Research." Journal of Speech, Language, and Hearing Research 62, no. 3 (March 25, 2019): 577–86. http://dx.doi.org/10.1044/2018_jslhr-h-astm-18-0228.

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Purpose This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides. Method First, we demonstrate the development of Bayes' theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach. Conclusion Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly. Supplemental Material https://doi.org/10.23641/asha.7822592

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Yuan, Ying, and Suyu Liu. "Abstract A034: Bayesian adaptive design for phase I drug combination trials." Cancer Research 82, no. 10_Supplement (May 15, 2022): A034. http://dx.doi.org/10.1158/1538-7445.evodyn22-a034.

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Abstract Drug combination therapy is the most commonly used approach to overcome acquired resistance to cancer therapy. Designing phase I drug combination trials faces several challenges beyond the conventional single-agent dose escalation studies, e.g., two-dimensional dose space and partial order in toxicity. In this presentation, we will overview novel Bayesian adaptive designs for drug combination trials. In particularly, we will focus on model-assisted designs that are simply to implement, while yielding superior operating characteristics. Trial example and software will be provided to illustrate the application of the novel designs. Citation Format: Ying Yuan, Suyu Liu. Bayesian adaptive design for phase I drug combination trials [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A034.

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Hubin, Aliaksandr, Geir Storvik, and Florian Frommlet. "Flexible Bayesian Nonlinear Model Configuration." Journal of Artificial Intelligence Research 72 (November 22, 2021): 901–42. http://dx.doi.org/10.1613/jair.1.13047.

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Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through flexible approaches such as neural networks, but this results in less interpretable models and potential overfitting. Alternatively, specific parametric nonlinear functions can be used, but the specification of such functions is in general complicated. In this paper, we introduce a flexible approach for the construction and selection of highly flexible nonlinear parametric regression models. Nonlinear features are generated hierarchically, similarly to deep learning, but have additional flexibility on the possible types of features to be considered. This flexibility, combined with variable selection, allows us to find a small set of important features and thereby more interpretable models. Within the space of possible functions, a Bayesian approach, introducing priors for functions based on their complexity, is considered. A genetically modi ed mode jumping Markov chain Monte Carlo algorithm is adopted to perform Bayesian inference and estimate posterior probabilities for model averaging. In various applications, we illustrate how our approach is used to obtain meaningful nonlinear models. Additionally, we compare its predictive performance with several machine learning algorithms.

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Alexander, Brian Michael, Patrick Y. Wen, Eudocia Quant Lee, Tracy Batchelor, Timothy Francis Cloughesy, Giovanni Parmigiani, and Lorenzo Trippa. "Bayesian adaptive randomized trial design for patients with recurrent glioblastoma." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 2005. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.2005.

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2005 Background: Bayesian-based trial design has the ability to utilize accumulating data in real time to alter the course of the trial, thereby enabling dynamic allocation to experimental arms and earlier dropping of ineffective arms. This flexibility results in a potentially more efficient trial framework by increasing the probability of enrollment to arms that show evidence of efficacy. In this study we considered a hypothetical scenario in which patients who have been previously treated on several separate experimental protocols at the Dana-Farber/Harvard Cancer Center and the University of California, Los Angeles were instead enrolled in a single multi-arm protocol utilizing a Bayesian adaptively randomized trial design. The purpose was to determine whether similar scientific results could have been accomplished more efficiently. Methods: We generated an adaptive randomization procedure that was retrospectively applied to primary patient data from four separate phase II clinical trials in patients with recurrent glioblastoma. We then compared AR to more conventional trial designs using realistic hypothetical scenarios consistent with survival data reported in the literature. Our primary endpoint is the number of patients needed to achieve a desired statistical power. Results: If our phase II trials had been a single multi-arm AR trial, bevacizumab would have been identified as an efficacious therapy, and 30 fewer patients would have been needed compared to a multi-arm balanced randomized (BR) design. More generally, Bayesian AR trial design for patients with glioblastoma would result in trials with fewer overall patients with no loss in statistical power, and in more patients randomized to effective treatment arms. For a trial with a control arm, two ineffective arms and one effective arm with hazard ratio 0.6, a median of 47 patients would be randomized to the effective arm compared with 35 in a BR design. Conclusions: Given the desire for control arms in phase II trials, an increasing number of experimental therapeutics for patients with glioblastoma, and a relatively short time for events, Bayesian adaptive designs are attractive for clinical trials in glioblastoma.

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Yu, Shiyong. "MatCalib: a Matlab software package for Bayesian modeling of radiocarbon ages subject to temporal order constraints." AIMS Geosciences 8, no. 1 (2022): 16–32. http://dx.doi.org/10.3934/geosci.2022002.

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<abstract> <p>Radiocarbon ages must be calibrated due to the remarkable fluctuations of the atmospheric radiocarbon level. The traditional method (e.g., Calib) does not make use of any constraint such as the temporal/stratigraphical ordering of the ages, thereby resulting in one or several large age ranges. Bayesian age modeling is advantageous over the traditional method in several aspects. First, it can provide precise age estimates by applying some constraints known <italic>a priori</italic>. Second, it may provide a timing of an archaeological feature or a geological event that is unable to be dated directly. Although several Bayesian age modeling frameworks have been developed, inexperienced users may need not only a more user-friendly environment for data entry and definition of their project-specific problem, but also a powerful post-processing tool for analyzing and visualizing the results. Here a hierarchical Bayesian model with a minimum level of structural complexity is presented. It provides users with a flexible and powerful framework to incorporate radiocarbon ages into a sequence along a one-dimensional continuum so that it best reveals their temporal order, thereby yielding a more precise timing. The accompanying Matlab software package not only complements the existing MatCal package designed to calibrate radiocarbon ages individually, but also serves as an alternative to the online tools of Bayesian radiocarbon age modeling such as OxCal and BCal.</p> </abstract>

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Bonilla, Diego A., Yurany Moreno, Camila Gho, Jorge L. Petro, Adrián Odriozola-Martínez, and Richard B. Kreider. "Effects of Ashwagandha (Withania somnifera) on Physical Performance: Systematic Review and Bayesian Meta-Analysis." Journal of Functional Morphology and Kinesiology 6, no. 1 (February 11, 2021): 20. http://dx.doi.org/10.3390/jfmk6010020.

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Ashwagandha (Withania somnifera) is considered a potent adaptogen and anti-stress agent that could have some potential to improve physical performance. This preferred reporting items for systematic reviews and meta-analyses (PRISMA)-based comprehensive systematic review and Bayesian meta-analysis aimed to evaluate clinical trials up to 2020 from PubMed, ScienceDirect, and Google Scholar databases regarding the effect of Ashwagandha supplementation on physical performance in healthy individuals. Besides implementing estimation statistics analysis, we developed Bayesian hierarchical models for a pre-specified subgroup meta-analysis on strength/power, cardiorespiratory fitness and fatigue/recovery variables. A total of 13 studies met the requirements of this systematic review, although only 12 were included in the quantitative analysis. A low-to-moderate overall risk of bias of the trials included in this study was detected. All Bayesian hierarchical models converged to a target distribution (Ȓ = 1) for both meta-analytic effect size (μ) and between-study standard deviation (τ). The meta-analytic approaches of the included studies revealed that Ashwagandha supplementation was more efficacious than placebo for improving variables related to physical performance in healthy men and female. In fact, the Bayesian models showed that future interventions might be at least in some way beneficial on the analyzed outcomes considering the 95% credible intervals for the meta-analytic effect size. Several practical applications and future directions are discussed, although more comparable studies are needed in exercise training, and athletic populations are needed to derive a more stable estimate of the true underlying effect.

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29

Young, Virginia R. "Robust Bayesian Credibility Using Semiparametric Models." ASTIN Bulletin 28, no. 2 (November 1998): 187–203. http://dx.doi.org/10.2143/ast.28.2.519065.

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AbstractIn performing Bayesian analysis of insurance losses, one usually chooses a parametric conditional loss distribution for each risk and a parametric prior distribution to describe how the conditional distributions vary across the risks. Young (1997) applies techniques from nonparametric density estimation to estimate the prior and uses the estimated model to calculate the predictive mean of future claims given past claims. A shortcoming of this method is that, in estimating the prior, one assumes the average claim amount equals the conditional claim. In this paper, we consider a class of priors obtained by perturbing the one determined nonparametrically, as in Young (1997). We thereby reflect the uncertainty in the prior that arises from the randomness in the claim data. We, then, calculate intervals for the corresponding predictive means. We illustrate our method with data from Dannenburg et al. (1996) and compare the intervals of the predictive means with nonparametric confidence intervals.

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30

Ryan, Elizabeth G., Ewen M. Harrison, Rupert M. Pearse, and Simon Gates. "Perioperative haemodynamic therapy for major gastrointestinal surgery: the effect of a Bayesian approach to interpreting the findings of a randomised controlled trial." BMJ Open 9, no. 3 (March 2019): e024256. http://dx.doi.org/10.1136/bmjopen-2018-024256.

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ObjectiveThe traditional approach of null hypothesis testing dominates the design and analysis of randomised controlled trials. This study aimed to demonstrate how a simple Bayesian analysis could have been used to analyse the Optimisation of Perioperative Cardiovascular Management to Improve Surgical Outcome (OPTIMISE) trial to obtain more clinically interpretable results.Design, setting, participants and interventionsThe OPTIMISE trial was a pragmatic, multicentre, observer-blinded, randomised controlled trial of 734 high-risk patients undergoing major gastrointestinal surgery in 17 acute care hospitals in the UK. Patients were randomly allocated to a cardiac output-guided haemodynamic therapy algorithm for intravenous fluid and inotropic drug administration during and in the 6 hours following surgery (n=368) or to standard care (n=366). The primary outcome was a binary outcome consisting of a composite of predefined 30-day moderate or major complications and mortality.MethodsWe repeated the primary outcome analysis of the OPTIMISE trial using Bayesian statistical methods to calculate the probability that the intervention was superior, and the probability that a clinically relevant difference existed. We explored the impact of a flat prior and an evidence-based prior on our analyses.ResultsAlthough OPTIMISE was not powered to detect a statistically significant difference between the treatment arms for the observed effect size (relative risk=0.84, 95% CI 0.70 to 1.01; p=0.07), by using Bayesian analyses we were able to demonstrate that there was a 96.9% (flat prior) to 99.5% (evidence-based prior) probability that the intervention was superior to the control.ConclusionsThe use of a Bayesian analytical approach provided a different interpretation of the findings of the OPTIMISE trial (compared with the original frequentist analysis), and suggested patient benefit from the intervention. Incorporation of information from previous studies provided further evidence of a benefit from the intervention. Bayesian analyses can produce results that are more easily interpretable and relevant to clinicians and policy-makers.Trial registration numberISRCTN04386758; Post-results.

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31

Leroy, Stephen S., Chi O. Ao, Olga P. Verkhoglyadova, and Mayra I. Oyola. "Analyzing the Diurnal Cycle by Bayesian Interpolation on a Sphere for Mapping GNSS Radio Occultation Data." Journal of Atmospheric and Oceanic Technology 38, no. 5 (May 2021): 951–61. http://dx.doi.org/10.1175/jtech-d-20-0031.1.

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AbstractBayesian interpolation has previously been proposed as a strategy to construct maps of radio occultation (RO) data, but that proposition did not consider the diurnal dimension of RO data. In this work, the basis functions of Bayesian interpolation are extended into the domain of the diurnal cycle, thus enabling monthly mapping of radio occultation data in synoptic time and analysis of the atmospheric tides. The basis functions are spherical harmonics multiplied by sinusoids in the diurnal cycle up to arbitrary spherical harmonic degree and diurnal cycle harmonic. Bayesian interpolation requires a regularizer to impose smoothness on the fits it produces, thereby preventing the overfitting of data. In this work, a formulation for the regularizer is proposed and the most probable values of the parameters of the regularizer determined. Special care is required when obvious gaps in the sampling of the diurnal cycle are known to occur in order to prevent the false detection of statistically significant high-degree harmonics of the diurnal cycle in the atmosphere. Finally, this work probes the ability of Bayesian interpolation to generate a valid uncertainty analysis of the fit. The postfit residuals of Bayesian interpolation are dominated not by measurement noise but by unresolved variability in the atmosphere, which is statistically nonuniform across the globe, thus violating the central assumption of Bayesian interpolation. The problem is ameliorated by constructing maps of RO data using Bayesian interpolation that partially resolve the temporal variability of the atmosphere, constructing maps for approximately every 3 days of RO data.

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Kitchen, Christina M. R., Sean Philpott, Harold Burger, Barbara Weiser, Kathryn Anastos, and Marc A. Suchard. "Evolution of Human Immunodeficiency Virus Type 1 Coreceptor Usage during Antiretroviral Therapy: a Bayesian Approach." Journal of Virology 78, no. 20 (October 15, 2004): 11296–302. http://dx.doi.org/10.1128/jvi.78.20.11296-11302.2004.

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ABSTRACT There is substantial evidence for ongoing replication and evolution of human immunodeficiency virus type 1 (HIV-1), even in individuals receiving highly active antiretroviral therapy. Viral evolution in the presence of antiviral therapy needs to be considered when developing new therapeutic strategies. Phylogenetic analyses of HIV-1 sequences can be used for this purpose but may give rise to misleading results if rates of intrapatient evolution differ significantly. To improve analyses of HIV-1 evolution relevant to studies of pathogenesis and treatment, we developed a Bayesian hierarchical model that incorporates all available sequence data while simultaneously allowing the phylogenetic parameters of each patient to vary. We used this method to examine evolutionary changes in HIV-1 coreceptor usage in response to treatment. We examined patients whose viral populations exhibited a shift in coreceptor utilization in response to therapy. CXCR4 (X4) strains emerged in each patient but were suppressed following initiation of new antiretroviral regimens, so that CCR5-utilizing (R5) strains predominated. By phylogenetically reconstructing the evolutionary relationship of HIV-1 obtained longitudinally from each patient, it was possible to examine the origin of the reemergent R5 virus. Using our Bayesian hierarchical approach, we found that the reemergent R5 virus detectable after therapy was more closely related to the predecessor R5 virus than to the X4 strains. The Bayesian hierarchical approach, unlike more traditional methods, makes it possible to evaluate competing hypotheses across patients. This model is not limited to analyses of HIV-1 but can be used to elucidate evolutionary processes for other organisms as well.

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33

Burgette, Lane F., and Susan M. Paddock. "Bayesian models for semicontinuous outcomes in rolling admission therapy groups." Psychological Methods 22, no. 4 (December 2017): 725–42. http://dx.doi.org/10.1037/met0000135.

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34

Svec, Jennifer M., Robert W. Coleman, Dennis R. Mungall, and Thomas M. Ludden. "Bayesian Pharmacokinetic/Pharmacodynamic Forecasting of Prothrombin Response to Warfarin Therapy." Therapeutic Drug Monitoring 7, no. 2 (June 1985): 174–80. http://dx.doi.org/10.1097/00007691-198506000-00006.

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35

Yet, Barbaros, Kaveh Bastani, Hendry Raharjo, Svante Lifvergren, William Marsh, and Bo Bergman. "Decision support system for Warfarin therapy management using Bayesian networks." Decision Support Systems 55, no. 2 (May 2013): 488–98. http://dx.doi.org/10.1016/j.dss.2012.10.007.

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36

Jensen, Shane T., Jared Park, Alexander F. Braunstein, and Jon Mcauliffe. "Bayesian Hierarchical Modeling of the HIV Evolutionary Response to Therapy." Journal of the American Statistical Association 108, no. 504 (December 2013): 1230–42. http://dx.doi.org/10.1080/01621459.2013.830449.

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37

Fleisher, Lee A., and W. Scott Beattie. "Perioperative statin therapy: understanding the evidence in a Bayesian context." Canadian Journal of Anesthesia/Journal canadien d'anesthésie 59, no. 6 (March 31, 2012): 511–15. http://dx.doi.org/10.1007/s12630-012-9704-x.

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38

Avent, M. L., and B. A. Rogers. "Optimising antimicrobial therapy through the use of Bayesian dosing programs." International Journal of Clinical Pharmacy 41, no. 5 (August 7, 2019): 1121–30. http://dx.doi.org/10.1007/s11096-019-00886-4.

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39

Baluev, Roman V. "Comparing the frequentist and Bayesian periodic signal detection: rates of statistical mistakes and sensitivity to priors." Monthly Notices of the Royal Astronomical Society 512, no. 4 (March 21, 2022): 5520–34. http://dx.doi.org/10.1093/mnras/stac762.

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ABSTRACT We perform extensive Monte Carlo simulations to systematically compare the frequentist and Bayesian treatments of the Lomb–Scargle periodogram. The goal is to investigate whether the Bayesian period search is advantageous over the frequentist one in terms of the detection efficiency, how much if yes, and how sensitive it is regarding the choice of the priors, in particular in case of a misspecified prior (whenever the adopted prior does not match the actual distribution of physical objects). We find that the Bayesian and frequentist analyses always offer nearly identical detection efficiency in terms of their trade-off between type-I and type-II mistakes. Bayesian detection may reveal a formal advantage if the frequency prior is non-uniform, but this results in only ∼1 per cent extra detected signals. In case if the prior was misspecified (adopting non-uniform one over the actual uniform) this may turn into an opposite advantage of the frequentist analysis. Finally, we revealed that Bayes factor of this task appears rather overconservative if used without a calibration against type-I mistakes (false positives), thereby necessitating such a calibration in practice.

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40

Liu, Kuan, Olli Saarela, Brian M. Feldman, and Eleanor Pullenayegum. "Estimation of causal effects with repeatedly measured outcomes in a Bayesian framework." Statistical Methods in Medical Research 29, no. 9 (January 29, 2020): 2507–19. http://dx.doi.org/10.1177/0962280219900362.

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Constructing causal inference methods to handle longitudinal data in observational studies is of high interest. In an observational setting, treatment assignment at each clinical visit follows a decision strategy where the treating clinician selects treatment based on current and past clinical measurements as well as treatment histories. These time-dependent structures, coupled with inherent correlations between and within each visit, add on to the data complexity. Despite recent interest in Bayesian causal methods, only a limited literature has explored approaches to handle longitudinal data and no method handles repeatedly measured outcomes. In this paper, we extended two Bayesian approaches: Bayesian estimation of marginal structural models and two-stage Bayesian propensity score analysis to handle a repeatedly measured outcome. Our proposed methods permit causal estimation of treatment effects at each visit. Time-dependent inverse probability of treatment weights are obtained from the Markov chain Monte Carlo samples of the posterior treatment assignment model for each follow-up visit. We use a simulation study to validate and compare the proposed methods and illustrate our approaches through a study of intravenous immunoglobulin therapy in treating newly diagnosed juvenile dermatomyositis.

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41

Kyougoku, Makoto, and Mutsumi Teraoka. "Bayesian Analysis of the Relationship Between Belief Conflict and Occupational Dysfunction." American Journal of Occupational Therapy 73, no. 6 (September 27, 2019): 7306205040p1. http://dx.doi.org/10.5014/ajot.2019.027615.

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42

Niven, Robert, Ali Mohammad-Djafari, Laurent Cordier, Markus Abel, and Markus Quade. "Bayesian Identification of Dynamical Systems." Proceedings 33, no. 1 (February 12, 2020): 33. http://dx.doi.org/10.3390/proceedings2019033033.

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Many inference problems relate to a dynamical system, as represented by dx/dt = f (x), where x ∈ ℝn is the state vector and f is the (in general nonlinear) system function or model. Since the time of Newton, researchers have pondered the problem of system identification: how should the user accurately and efficiently identify the model f – including its functional family or parameter values – from discrete time-series data? For linear models, many methods are available including linear regression, the Kalman filter and autoregressive moving averages. For nonlinear models, an assortment of machine learning tools have been developed in recent years, usually based on neural network methods, or various classification or order reduction schemes. The first group, while very useful, provide “black box" solutions which are not readily adaptable to new situations, while the second group necessarily involve the sacrificing of resolution to achieve order reduction. To address this problem, we propose the use of an inverse Bayesian method for system identification from time-series data. For a system represented by a set of basis functions, this is shown to be mathematically identical to Tikhonov regularization, albeit with a clear theoretical justification for the residual and regularization terms, respectively as the negative logarithms of the likelihood and prior functions. This insight justifies the choice of regularization method, and can also be extended to access the full apparatus of the Bayesian inverse solution. Two Bayesian methods, based on the joint maximum a posteriori (JMAP) and variational Bayesian approximation (VBA), are demonstrated for the Lorenz equation system with added Gaussian noise, in comparison to the regularization method of least squares regression with thresholding (the SINDy algorithm). The Bayesian methods are also used to estimate the variances of the inferred parameters, thereby giving the estimated model error, providing an important advantage of the Bayesian approach over traditional regularization methods.

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43

Chrystyn, Henry. "Validation of the use of Bayesian Analysis in the Optimization of Gentamicin Therapy from the Commencement of Dosing." Drug Intelligence & Clinical Pharmacy 22, no. 1 (January 1988): 49–53. http://dx.doi.org/10.1177/106002808802200112.

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A computer program based on the statistical technique of Bayesian analysis has been adapted to run on several microcomputers. The clinical application of this method for gentamicin has been validated in 13 patients with varying degrees of renal function by a comparison of the accuracy of this method to a predictive algorithm method and one using standard pharmacokinetic principles. Blood samples for serum gentamicin analysis were taken after the administraiton of an intravenous loading dose of gentamicin. The results produced by each method were used to predict the peak and trough values measured on day 3 of therapy. Of the three methods studied, Bayesian analysis, using a serum gentamicin concentration drawn four hours after the initial dose, was the least biased and the most precise method for predicting the observed levels. The mean prediction error of the Bayesian analysis method, using the four-hour sample, was −0.03 mg/L for the peak serum concentration and −0.07 mg/L for the trough level on day 3. Using this method the corresponding root mean squared prediction error was 0.60 mg/L and 0.36 mg/L for the peak and trough levels, respectively.

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44

Li, Wei, Philippe Ciais, Yilong Wang, Shushi Peng, Grégoire Broquet, Ashley P. Ballantyne, Josep G. Canadell, et al. "Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets." Proceedings of the National Academy of Sciences 113, no. 46 (October 31, 2016): 13104–8. http://dx.doi.org/10.1073/pnas.1603956113.

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Conventional calculations of the global carbon budget infer the land sink as a residual between emissions, atmospheric accumulation, and the ocean sink. Thus, the land sink accumulates the errors from the other flux terms and bears the largest uncertainty. Here, we present a Bayesian fusion approach that combines multiple observations in different carbon reservoirs to optimize the land (B) and ocean (O) carbon sinks, land use change emissions (L), and indirectly fossil fuel emissions (F) from 1980 to 2014. Compared with the conventional approach, Bayesian optimization decreases the uncertainties in B by 41% and in O by 46%. The L uncertainty decreases by 47%, whereas F uncertainty is marginally improved through the knowledge of natural fluxes. Both ocean and net land uptake (B + L) rates have positive trends of 29 ± 8 and 37 ± 17 Tg C⋅y−2 since 1980, respectively. Our Bayesian fusion of multiple observations reduces uncertainties, thereby allowing us to isolate important variability in global carbon cycle processes.

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45

Wiśniewski, Tomasz P. "Should income be taken for granted as a sole driver of welfare? Bayesian insight on the relevance of non-income drivers of welfare." International Journal of Management and Economics 54, no. 1 (March 29, 2018): 58–68. http://dx.doi.org/10.2478/ijme-2018-0006.

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Abstract Abstract: The paper consists of a discussion on the relevance of non-income drivers of welfare. This discussion is based on a subjective Bayesian reasoning, where welfare perceptions are subjectively rational decisions of individuals, who are, as author suggests, the ultimate decision-makers in respect of what welfare actually means for them. The objective of the paper is to investigate if income should be taken for granted as a sole driver of welfare. The conclusion is drawn from a methodological investigation of this question in a Bayesian concept of probability with a consideration for correlations among income and non-income drivers of welfare. It suggests that income should not be taken for granted as a sole driver of welfare since the non-income factors, which are not correlated with income, appear to be relevantly affecting individuals’ perceptions of welfare with Bayesian probability of almost 65%. Thereby, the paper is a reaffirmation of a need for further research in the area of welfare measures that might constitute an alternative to income-dominated indicators. Its value emanates from unambiguous answer in favour of the relevance of non-income drivers across welfare perceptions, which, without Bayesian reasoning, could remain unsolved at the point of 50% odds for relevance (irrelevance).

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Zhou, Wei-Neng, Yan-Min Zhang, Xin Qiao, Jing Pan, Ling-Feng Yin, Lu Zhu, Jun-Nan Zhao, et al. "Virtual Screening Strategy Combined Bayesian Classification Model, Molecular Docking for Acetyl-CoA Carboxylases Inhibitors." Current Computer-Aided Drug Design 15, no. 3 (April 10, 2019): 193–205. http://dx.doi.org/10.2174/1573409914666181109110030.

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Introduction: Acetyl-CoA Carboxylases (ACC) have been an important target for the therapy of metabolic syndrome, such as obesity, hepatic steatosis, insulin resistance, dyslipidemia, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), type 2 diabetes (T2DM), and some other diseases. Methods: In this study, virtual screening strategy combined with Bayesian categorization modeling, molecular docking and binding site analysis with protein ligand interaction fingerprint (PLIF) was adopted to validate some potent ACC inhibitors. First, the best Bayesian model with an excellent value of Area Under Curve (AUC) value (training set AUC: 0.972, test set AUC: 0.955) was used to screen compounds of validation library. Then the compounds screened by best Bayesian model were further screened by molecule docking again. Results: Finally, the hit compounds evaluated with four percentages (1%, 2%, 5%, 10%) were verified to reveal enrichment rates for the compounds. The combination of the ligandbased Bayesian model and structure-based virtual screening resulted in the identification of top four compounds which exhibited excellent IC 50 values against ACC in top 1% of the validation library. Conclusion: In summary, the whole strategy is of high efficiency, and would be helpful for the discovery of ACC inhibitors and some other target inhibitors.</P>

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47

Zou, Xin, and Wen Long Yue. "A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica." Journal of Advanced Transportation 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/2525481.

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Based on an overall consideration of factors affecting road safety evaluations, the Bayesian network theory based on probability risk analysis was applied to the causation analysis of road accidents. By taking Adelaide Central Business District (CBD) in South Australia as a case, the Bayesian network structure was established by integrating K2 algorithm with experts’ knowledge, and Expectation-Maximization algorithm that could process missing data was adopted to conduct the parameter learning in Netica, thereby establishing the Bayesian network model for the causation analysis of road accidents. Then Netica was used to carry out posterior probability reasoning, the most probable explanation, and inferential analysis. The results showed that the Bayesian network model could effectively explore the complex logical relation in road accidents and express the uncertain relation among related variables. The model not only can quantitatively predict the probability of an accident in certain road traffic condition but also can find the key reasons and the most unfavorable state combination which leads to the occurrence of an accident. The results of the study can provide theoretical support for urban road management authorities to thoroughly analyse the induction factors of road accidents and then establish basis in improving the safety performance of the urban road traffic system.

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Ren, Hongjia, Xianchang Wang, Hongbo Ren, and Qiulin Guo. "Spatial Distribution Prediction of Oil and Gas Based on Bayesian Network with Case Study." Mathematical Problems in Engineering 2020 (August 27, 2020): 1–9. http://dx.doi.org/10.1155/2020/4986563.

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Effectively predicting the spatial distribution of oil and gas contributes to delineating promising target areas for further exploration. Determining the location of hydrocarbon is a complex and uncertain decision problem. This paper proposes a method for predicting the spatial distribution of oil and gas resource based on Bayesian network. In this method, qualitative dependency relationship between the hydrocarbon occurrence and key geologic factors is obtained using Bayesian network structure learning by integrating the available geoscience information and the current exploration results and then using Bayesian network topology structure to predict the probability of hydrocarbon occurrence in the undiscovered area; finally, the probability map of hydrocarbon-bearing is formed by interpolation method. The proposed method and workflow are further illustrated using an example from the Carboniferous Huanglong Formation (C2hl) in the eastern part of the Sichuan Basin in China. The prediction results show that the coincidence rate between the results of 248 known exploration wells and the predicted results reaches 89.5%, and it has been found that the gas fields are basically located in the high value area of the hydrocarbon-bearing probability map. The application results show that the Bayesian network method can effectively predict the spatial distribution of oil and gas resources, thereby reducing exploration risks, optimizing exploration targets, and improving exploration benefits.

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49

Andrieu, Christophe, Nando de Freitas, and Arnaud Doucet. "Robust Full Bayesian Learning for Radial Basis Networks." Neural Computation 13, no. 10 (October 1, 2001): 2359–407. http://dx.doi.org/10.1162/089976601750541831.

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We propose a hierarchical full Bayesian model for radial basis networks. This model treats the model dimension (number of neurons), model parameters, regularization parameters, and noise parameters as unknown random variables. We develop a reversible-jump Markov chain Monte Carlo (MCMC) method to perform the Bayesian computation. We find that the results obtained using this method are not only better than the ones reported previously, but also appear to be robust with respect to the prior specification. In addition, we propose a novel and computationally efficient reversible-jump MCMC simulated annealing algorithm to optimize neural networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima to a large extent. We show that by calibrating the full hierarchical Bayesian prior, we can obtain the classical Akaike information criterion, Bayesian information criterion, and minimum description length model selection criteria within a penalized likelihood framework. Finally, we present a geometric convergence theorem for the algorithm with homogeneous transition kernel and a convergence theorem for the reversible-jump MCMC simulated annealing method.

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50

VENTER, JACOBUS PETRUS, and CORNELIS CRISTO VAN WAVEREN. "NEW PRODUCT DEVELOPMENT WITH DYNAMIC DECISION SUPPORT." International Journal of Innovation and Technology Management 06, no. 02 (June 2009): 135–53. http://dx.doi.org/10.1142/s0219877009001601.

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The development of new and improved management methods for new product development is important. Existing methods suffer from a number of shortcomings, especially their inability to deal with a mixture of quantitative and qualitative data. The objective of this study is to apply decision support techniques (especially Bayesian networks) to the area of new product development management in order to address some of the shortcomings. The research approach is one of decision structuring and modeling. The literature shows the criteria that are important during the management of new product development. These criteria are used in a three-step decision structuring framework to develop a conceptual model based on a Bayesian network, in support of new product development management. The result is a Bayesian network that incorporates the knowledge of experts into a decision support model. The model is shown to be requisite because it contains all the essential elements of the problem on which decision-makers can base their action. The model can be used to perform 'what-if' analyses in various ways, thereby supporting the management of risk in new product development. This research not only contributes a model to support new product development management, but also provides insight into how decision support — especially Bayesian networks — can enhance technology management methods.

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