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Literatura académica sobre el tema "Bayesian Simulated Inference"

Autor: Grafiati

Publicado: 10 de marzo de 2023

Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Bayesian Simulated Inference"

Beaumont, Mark A., Wenyang Zhang, and David J. Balding. "Approximate Bayesian Computation in Population Genetics." Genetics 162, no. 4 (2002): 2025–35. http://dx.doi.org/10.1093/genetics/162.4.2025.

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Abstract We propose a new method for approximate Bayesian statistical inference on the basis of summary statistics. The method is suited to complex problems that arise in population genetics, extending ideas developed in this setting by earlier authors. Properties of the posterior distribution of a parameter, such as its mean or density curve, are approximated without explicit likelihood calculations. This is achieved by fitting a local-linear regression of simulated parameter values on simulated summary statistics, and then substituting the observed summary statistics into the regression equa

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Creel, Michael. "Inference Using Simulated Neural Moments." Econometrics 9, no. 4 (2021): 35. http://dx.doi.org/10.3390/econometrics9040035.

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This paper studies method of simulated moments (MSM) estimators that are implemented using Bayesian methods, specifically Markov chain Monte Carlo (MCMC). Motivation and theory for the methods is provided by Chernozhukov and Hong (2003). The paper shows, experimentally, that confidence intervals using these methods may have coverage which is far from the nominal level, a result which has parallels in the literature that studies overidentified GMM estimators. A neural network may be used to reduce the dimension of an initial set of moments to the minimum number that maintains identification, as

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Flury, Thomas, and Neil Shephard. "BAYESIAN INFERENCE BASED ONLY ON SIMULATED LIKELIHOOD: PARTICLE FILTER ANALYSIS OF DYNAMIC ECONOMIC MODELS." Econometric Theory 27, no. 5 (2011): 933–56. http://dx.doi.org/10.1017/s0266466610000599.

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We note that likelihood inference can be based on an unbiased simulation-based estimator of the likelihood when it is used inside a Metropolis–Hastings algorithm. This result has recently been introduced in statistics literature by Andrieu, Doucet, and Holenstein (2010, Journal of the Royal Statistical Society, Series B, 72, 269–342) and is perhaps surprising given the results on maximum simulated likelihood estimation. Bayesian inference based on simulated likelihood can be widely applied in microeconomics, macroeconomics, and financial econometrics. One way of generating unbiased estimates o

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Hu, Zheng Dong, Liu Xin Zhang, Fei Yue Zhou, and Zhi Jun Li. "Statistic Inference for Inertial Instrumentation Error Model Using Bayesian Network." Applied Mechanics and Materials 392 (September 2013): 719–24. http://dx.doi.org/10.4028/www.scientific.net/amm.392.719.

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For the parameter estimation problem of inertial instrumentation error models, a Bayesian network is founded to fuse the calibration data and make error coefficients statistical inference in this paper. First the fundamental of Bayesian network is stated and then how to establish network for a typical case of inertial instrumentation error coefficients estimation is illustrated. Since the difficult high-dimension integral calculus for model parameter can be avoidable, WinBUGS software based on MCMC method is used for calculation and inference. The simulated results show that using Bayesian net

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Jeffrey, Niall, and Filipe B. Abdalla. "Parameter inference and model comparison using theoretical predictions from noisy simulations." Monthly Notices of the Royal Astronomical Society 490, no. 4 (2019): 5749–56. http://dx.doi.org/10.1093/mnras/stz2930.

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ABSTRACT When inferring unknown parameters or comparing different models, data must be compared to underlying theory. Even if a model has no closed-form solution to derive summary statistics, it is often still possible to simulate mock data in order to generate theoretical predictions. For realistic simulations of noisy data, this is identical to drawing realizations of the data from a likelihood distribution. Though the estimated summary statistic from simulated data vectors may be unbiased, the estimator has variance that should be accounted for. We show how to correct the likelihood in the

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de Campos, Luis M., José A. Gámez, and Serafı́n Moral. "Partial abductive inference in Bayesian belief networks by simulated annealing." International Journal of Approximate Reasoning 27, no. 3 (2001): 263–83. http://dx.doi.org/10.1016/s0888-613x(01)00043-3.

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CARDIAL, Marcílio Ramos Pereira, Juliana Betini FACHINI-GOMES, and Eduardo Yoshio NAKANO. "EXPONENTIATED DISCRETE WEIBULL DISTRIBUTION FOR CENSORED DATA." REVISTA BRASILEIRA DE BIOMETRIA 38, no. 1 (2020): 35. http://dx.doi.org/10.28951/rbb.v38i1.425.

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This paper further develops the statistical inference procedure of the exponentiated discrete Weibull distribution (EDW) for data with the presence of censoring. This generalization of the discrete Weibull distribution has the advantage of being suitable to model non-monotone failure rates, such as those with bathtub and unimodal distributions. Inferences about EDW distribution are presented using both frequentist and bayesian approaches. In addition, the classical Likelihood Ratio Test and a Full Bayesian Signiﬁcance Test (FBST) were performed to test the parameters of EDW distribution. The m

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Sha, Naijun, and Hao Yang Teng. "A Bayes Inference for Step-Stress Accelerated Life Testing." International Journal of Statistics and Probability 6, no. 6 (2017): 1. http://dx.doi.org/10.5539/ijsp.v6n6p1.

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In this article, we present a Bayesian analysis with convex tent priors for step-stress accelerated life testing (SSALT) using a proportional hazard (PH) model. As flexible as the cumulative exposure (CE) model in fitting step-stress data and its attractive mathematical properties, the PH model makes Bayesian inference much more accessible than the CE model. Two sampling methods through Markov chain Monte Carlo algorithms are employed for posterior inference of parameters. The performance of the methodology is investigated using both simulated and real data sets.

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Bevilacqua, Vitoantonio, Giuseppe Mastronardi, Filippo Menolascina, Paolo Pannarale, and Giuseppe Romanazzi. "Bayesian Gene Regulatory Network Inference Optimization by means of Genetic Algorithms." JUCS - Journal of Universal Computer Science 15, no. (4) (2009): 826–39. https://doi.org/10.3217/jucs-015-04-0826.

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Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When time-course data is available, gene interactions may be modeled by a Bayesian Network (BN). Given a structure, that models the conditional independence between genes, we can tune the parameters in a way that maximize the likelihood of the observed data. The structure that best fit the observed data reflects the real gene network's connections. Well known learning algorithms (greedy search and simulated annealing) devoted to BN structure learning have been used in literature

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Azzolina, Danila, Giulia Lorenzoni, Silvia Bressan, Liviana Da Dalt, Ileana Baldi, and Dario Gregori. "Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach." International Journal of Environmental Research and Public Health 18, no. 4 (2021): 2095. http://dx.doi.org/10.3390/ijerph18042095.

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In the conduction of trials, a common situation is related to potential difficulties in recruiting the planned sample size as provided by the study design. A Bayesian analysis of such trials might provide a framework to combine prior evidence with current evidence, and it is an accepted approach by regulatory agencies. However, especially for small trials, the Bayesian inference may be severely conditioned by the prior choices. The Renal Scarring Urinary Infection (RESCUE) trial, a pediatric trial that was a candidate for early termination due to underrecruitment, served as a motivating exampl

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Tesis sobre el tema "Bayesian Simulated Inference"

Mezzavilla, Marco. "Advanced Resource Management Techniques for Next Generation Wireless Networks." Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3423728.

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The increasing penetration of mobile devices in everyday life is posing a broad range of research challenges to meet such a massive data demand. Mobile users seek connectivity "anywhere, at anytime". In addition, killer applications with multimedia contents, like video transmissions, require larger amounts of resources to cope with tight quality constraints. Spectrum scarcity and interference issues represent the key aspects of next generation wireless networks. Consequently, designing proper resource management solutions is critical. To this aim, we first propose a model to better assess the

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Kolba, Mark Philip. "Information-Based Sensor Management for Static Target Detection Using Real and Simulated Data." Diss., 2009. http://hdl.handle.net/10161/1313.

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<p>In the modern sensing environment, large numbers of sensor tasking decisions must be made using an increasingly diverse and powerful suite of sensors in order to best fulfill mission objectives in the presence of situationally-varying resource constraints. Sensor management algorithms allow the automation of some or all of the sensor tasking process, meaning that sensor management approaches can either assist or replace a human operator as well as ensure the safety of the operator by removing that operator from a dangerous operational environment. Sensor managers also provide improved sys

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Viscardi, Cecilia. "Approximate Bayesian Computation and Statistical Applications to Anonymized Data: an Information Theoretic Perspective." Doctoral thesis, 2021. http://hdl.handle.net/2158/1236316.

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Realistic statistical modelling of complex phenomena often leads to considering several latent variables and nuisance parameters. In such cases, the Bayesian approach to inference requires the computation of challenging integrals or summations over high dimensional spaces. Monte Carlo methods are a class of widely used algorithms for performing simulated inference. In this thesis, we consider the problem of sample degeneracy in Monte Carlo methods focusing on Approximate Bayesian Computation (ABC), a class of likelihood-free algorithms allowing inference when the likelihood function is anal

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Capítulos de libros sobre el tema "Bayesian Simulated Inference"

Lin, Luan, and Jun Zhu. "Using Simulated Data to Evaluate Bayesian Network Approach for Integrating Diverse Data." In Gene Network Inference. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45161-4_8.

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Harrow, Aram W., and Annie Y. Wei. "Adaptive Quantum Simulated Annealing for Bayesian Inference and Estimating Partition Functions." In Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2020. http://dx.doi.org/10.1137/1.9781611975994.12.

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Santner, Thomas J., Brian J. Williams, and William I. Notz. "Bayesian Inference for Simulator Output." In Springer Series in Statistics. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8847-1_4.

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Stewart, Donal, Stephen Gilmore, and Michael A. Cousin. "FM-Sim: A Hybrid Protocol Simulator of Fluorescence Microscopy Neuroscience Assays with Integrated Bayesian Inference." In Hybrid Systems Biology. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27656-4_10.

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Jirsa, Ladislav, Anthony Quinn, and Ferdinand Varga. "Identification of Thyroid Gland Activity in Radiotherapy*." In Bayesian Statistics 8. Oxford University PressOxford, 2007. http://dx.doi.org/10.1093/oso/9780199214655.003.0029.

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Abstract The Bayesian identification of a linear regression model for time dependence of thyroid gland activity in 131I radiotherapy is presented. Prior knowledge is elicited via hard parameter constraints and via the merging of information from an archive of patient records. This prior regularization is shown to be crucial in the reported context, where data typically comprise only 2-3 highnoise measurements. The posterior distribution is simulated via a Langevin diffusion algorithm, whose optimization for the thyroid activity application is explained. Improved patient-specific predictions of

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Keskin, Fesih. "Nested Sampling: A Case Study in Parameter Estimation." In Bayesian Inference - Recent Trends [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1003926.

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Nested Sampling (NS) is a powerful Bayesian inference algorithm that can be used to estimate parameter posteriors and marginal likelihoods for complex models. It is a sequential algorithm that works by iteratively removing low-likelihood regions of the parameter space while keeping track of the weights of the remaining points. This allows NS to efficiently sample the posterior distribution, even for models with complex and multimodal posteriors. NS has been used to estimate parameters in a wide range of applications, including cosmology, astrophysics, biology, and machine learning. It is parti

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Davy, Manuel, and Simon J. Godsill. "Bayesian Harmonic Models for Musical Signal Analysis." In Bayesian Statistics 7. Oxford University PressOxford, 2003. http://dx.doi.org/10.1093/oso/9780198526155.003.0006.

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Abstract This paper is concerned with the Bayesian analysis of musical signals. The ultimate aim is to use Bayesian hierarchical structures in order to infer quantities at the highest level, including such things as musical pitch, dynamics, timbre, instrument identity, etc. Analysis of real musical signals is complicated by many things, including the presence of transient sounds, noises, and the complex structure of musical pitches in the frequency domain. The problem is truly Bayesian in that there is a wealth of (often subjective) prior knowledge about how musical signals are constructed, wh

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Fernández Antonio, Pérez-Bernabé Inmaculada, Rumí Rafael, and Salmerón Antonio. "Incorporating Prior Knowledge when Learning Mixtures of Truncated Basis Functions from Data." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-330-8-95.

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Mixtures of truncated basis functions (MoTBFs) have been recently proposed as a generalisation of mixtures of truncated exponentials and mixtures of polynomials for modelling univariate and conditional distributions in hybrid Bayesian networks. In this paper we analyse the problem of incorporating prior knowledge when learning univariate MoTBFs. We consider scenarios where the prior knowledge is expressed as an MoTBF that is combined with another MoTBF density estimated from the available data. An important property, from the point of view of inference in hybrid Bayesian networks, is that the

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Radde, Nicole, and Lars Kaderali. "A Bayes Regularized Ordinary Differential Equation Model for the Inference of Gene Regulatory Networks." In Handbook of Research on Computational Methodologies in Gene Regulatory Networks. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-685-3.ch006.

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Differential equation models provide a detailed, quantitative description of transcription regulatory networks. However, due to the large number of model parameters, they are usually applicable to small networks only, with at most a few dozen genes. Moreover, they are not well suited to deal with noisy data. In this chapter, we show how to circumvent these limitations by integrating an ordinary differential equation model into a stochastic framework. The resulting model is then embedded into a Bayesian learning approach. We integrate the-biologically motivated-expectation of sparse connectivit

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Hoang, Nu, Bao Duong, and Thin Nguyen. "Scalable Variational Causal Discovery Unconstrained by Acyclicity." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240801.

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Bayesian causal discovery offers the power to quantify epistemic uncertainties among a broad range of structurally diverse causal theories potentially explaining the data, represented in forms of directed acyclic graphs (DAGs). However, existing methods struggle with efficient DAG sampling due to the complex acyclicity constraint. In this study, we propose a scalable Bayesian approach to effectively learn the posterior distribution over causal graphs given observational data thanks to the ability to generate DAGs without explicitly enforcing acyclicity. Specifically, we introduce a novel diffe

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Actas de conferencias sobre el tema "Bayesian Simulated Inference"

Andrianov, S. A. "Utilizing Bayesian Inference for Pulsar Exoplanets Finding." In 52-st All-Russian with international participation student scientific conference "Physics of Space". Ural University Press, 2025. https://doi.org/10.15826/b978-5-7996-3986-0.12.

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This work describes a methodology of hypothesis testing for possible existence of a body orbiting a pulsar. The methodology based on Bayesian inference. The tested hypotheses are a single pulsar, that exhibits power-law red noise in timing residuals, and a binary pulsar with white measurement noise. The methodology was tested on a series of simulated data and was used for analysis of pulsar’s B0329+54 timing residuals.

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Yousefian, Sajjad, Gilles Bourque, Sandeep Jella, Philippe Versailles, and Rory F. D. Monaghan. "A Stochastic and Bayesian Inference Toolchain for Uncertainty and Risk Quantification of Rare Autoignition Events in DLE Premixers." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-83667.

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Abstract Quantification of aleatoric uncertainties due to the inherent variabilities in operating conditions and fuel composition is essential for designing and improving premixers in dry low-emissions (DLE) combustion systems. Advanced stochastic simulation tools require a large number of evaluations in order to perform this type of uncertainty quantification (UQ) analysis. This task is computationally prohibitive using high-fidelity computational fluid dynamic (CFD) approaches such as large eddy simulation (LES). In this paper, we describe a novel and computationally-efficient toolchain for

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Palencia, O. G., A. P. Teixeira, and C. Guedes Soares. "Safety of Pipelines Subjected to Deterioration Processes Modelled Through Dynamic Bayesian Networks." In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/omae2017-61969.

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The paper studies the application of Dynamic Bayesian Networks for modelling degradation processes in oil and gas pipelines. A DBN tool consisting of a Matlab code has been developed for performing inference on models. The tool is then applied for probabilistic modelling of the burst pressure of a pipe subjected to corrosion degradation and for safety assessment. The burst pressure is evaluated using the ASME B31G design method and other empirical formulas. A model for corrosion prediction in pipelines and its governing parameters are explicitly included into the probabilistic framework. Diffe

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Losi, Enzo, Mauro Venturini, and Lucrezia Manservigi. "Gas Turbine Health State Prognostics by Means of Bayesian Hierarchical Models." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90054.

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Abstract The prediction of the time evolution of gas turbine performance is an emerging requirement of modern prognostics and health management (PHM), aimed at improving system reliability and availability, while reducing life cycle costs. In this work, a data-driven Bayesian Hierarchical Model (BHM) is employed to perform a probabilistic prediction of gas turbine future health state thanks to its capability to deal with fleet data from multiple units. First, the theoretical background of the predictive methodology is outlined to highlight the inference mechanism and data processing for estima

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Liu, Mengchen, Liu Jiang, Junlin Liu, Xiting Wang, Jun Zhu, and Shixia Liu. "Improving Learning-from-Crowds through Expert Validation." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/324.

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Although several effective learning-from-crowd methods have been developed to infer correct labels from noisy crowdsourced labels, a method for post-processed expert validation is still needed. This paper introduces a semi-supervised learning algorithm that is capable of selecting the most informative instances and maximizing the influence of expert labels. Specifically, we have developed a complete uncertainty assessment to facilitate the selection of the most informative instances. The expert labels are then propagated to similar instances via regularized Bayesian inference. Experiments on b

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Di Francesco, Domenic, Marios Chryssanthopoulos, Michael Havbro Faber, and Ujjwal Bharadwaj. "Bayesian Multi-Level Modelling for Improved Prediction of Corrosion Growth Rate." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18744.

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Abstract In pipelines, pressure vessels and various other steel structures, the remaining thickness of a corroding ligament can be measured directly and repeatedly over time. Statistical analysis of these measurements is a common approach for estimating the rate of corrosion growth, where the uncertainties associated with the inspection activity are taken into account. An additional source of variability in such calculations is the epistemic uncertainty associated with the limited number of measurements that are available to engineers at any point in time. Traditional methods face challenges i

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Profir, B., M. H. Eres, J. P. Scanlan, and R. Bates. "Investigation of Fan Blade off Events Using a Bayesian Framework." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-63431.

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This paper illustrates a probabilistic method of studying Fan Blade Off (FBO) events which is based upon Bayesian inference. Investigating this case study is of great interest from the point of view of the engineering team responsible with the dynamic modelling of the fan. The reason is because subsequent to an FBO event, the fan loses its axisymmetry and as a result of that, severe impacting can occur between the blades and the inner casing of the engine. The mechanical modelling (which is not the scope of this paper) involves studying the oscillation modes of the fan at various release speed

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Hou, Danlin, Chang Shu, Lili Ji, Ibrahim Galal Hassan, and Liangzhu (Leon) Wang. "Bayesian Calibrating Educational Building Thermal Models to Hourly Indoor Air Temperature: Methodology and Case Study." In ASME 2021 Verification and Validation Symposium. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/vvs2021-65268.

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Abstract With the increase in the frequency and duration of heatwaves and extreme temperatures, global warming becomes one of the most critical environmental issues. Heatwaves pose significant threats to human health, including related diseases and deaths, especially for vulnerable groups. Such as the one during the 2018 summer in Montreal, Canada, caused up to 53 deaths, with most lived in buildings without access to air-conditioning. Unlike building energy models that mainly focus on energy performance, building thermal models emphasizes indoor thermal performance without a mechanical system

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Gnanasekaran, N., and C. Balaji. "A Correlation for Nusselt Number Under Turbulent Mixed Convection Using Transient Heat Transfer Experiments." In 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-22428.

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This paper reports the results of an experimental investigation of transient, turbulent mixed convection in a vertical channel in which one of the walls is heated and the other is adiabatic. The goal is to simultaneously estimate the constants in a Nusselt number correlation whose form is assumed a priori by synergistically marrying the experimental results with repeated numerical calculations that assume guess values of the constants. The convective heat transfer coefficient “h” is replaced by the Nusselt number (Nu) which is then assumed to have a form Nu = a (1+RiD) b ReDc where a, b and c

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Bisinotto, Gustavo A., Lucas P. Cotrim, Fabio G. Cozman, and Eduardo A. Tannuri. "Assessment of Sea State Estimation With Convolutional Neural Networks Based on the Motion of a Moored FPSO Subjected to High-Frequency Wave Excitation." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-78603.

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Abstract Motion-based wave inference has been extensively discussed over the past years to estimate sea state parameters from the measured motions of a vessel. Most of those methods rely on the linearity assumption between waves and ship response and present a limitation related to high-frequency waves, whose first-order excitation is mostly filtered by the vessel. In a previous study in this project, the motion of a spread-moored FPSO platform, associated with a dataset of environmental conditions, was used to train convolutional neural networks models so as to estimate sea state parameters,

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