Bayesian Monte Carlo method

Part 1 of ebook "Bayesian essentials with R (Second edition)" provides readers with contents including: Chapter 1 - User's manual; Chapter 2 - Normal models; Chapter 3 - Regression and variable selection; Chapter 4 - Generalized linear models;...

151p daonhiennhien 03-07-2024 3 1   Download

This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods.

489p vimeyers 29-05-2024 2 2   Download

In this paper, the research done is about the risk distribution model estimation on health insurance claims using Bayesian. The objective is to derive a health insurance risk model and determine the amount of net premium for each insured age group in health insurance. The sample of this study is the participant of health insurance in the Bandung area, Indonesia, especially for the insured who live in flood-prone areas.

10p longtimenosee10 26-04-2024 3 1   Download

Single-molecule RNA fluorescence in situ hybridization (smFISH) provides unparalleled resolution in the measurement of the abundance and localization of nascent and mature RNA transcripts in fixed, single cells. We developed a computational pipeline (BayFish) to infer the kinetic parameters of gene expression from smFISH data at multiple time points after gene induction.

12p vialfrednobel 29-01-2022 11 0   Download

Although human leukocyte antigen (HLA) genotyping based on amplicon, whole exome sequence (WES), and RNA sequence data has been achieved in recent years, accurate genotyping from whole genome sequence (WGS) data remains a challenge due to the low depth. Furthermore, there is no method to identify the sequences of unknown HLA types not registered in HLA databases.

11p vitzuyu2711 29-09-2021 10 1   Download

In previous studies, the prior probability has been considered as a fixed value only, hence, the Bayes error is usually a fixed value. This sometimes leads to unreasonable results. To fill the mentioned research gap, this paper considers the prior probability q in Bayesian classifier as a distribution, and looks insight the posterior distribution of Bayes error, using Monte-Carlo simulation. Finally, the proposed method is applied to credit scoring data of a bank in Vietnam. Based on the results, we can determine whether the Bayesian classifier is suitable for data or not.

10p angicungduoc9 04-01-2021 10 1   Download

MCMC-based methods are important for Bayesian inference of phylogeny and related parameters. Although being computationally expensive, MCMC yields estimates of posterior distributions that are useful for estimating parameter values and are easy to use in subsequent analysis.

8p vioklahoma2711 19-11-2020 9 0   Download

Running multiple-chain Markov Chain Monte Carlo (MCMC) provides an efficient parallel computing method for complex Bayesian models, although the efficiency of the approach critically depends on the length of the non-parallelizable burn-in period, for which all simulated data are discarded.

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This paper presents a Bayesian approach based on integral experiments to create correlations between different isotopes which do not appear with differential data. A simple Bayesian set of equations is presented with random nuclear data, similarly to the usual methods applied with differential data.

10p christabelhuynh 29-05-2020 8 1   Download

This paper gives an overview of the evaluation processes used for nuclear data at CEA. After giving Bayesian inference and associated methods used in the CONRAD code [P. Archier et al., Nucl. Data Sheets 118, 488 (2014)], a focus on systematic uncertainties will be given.

8p christabelhuynh 29-05-2020 6 1   Download

Assuming different non-informative prior distributions for the parameters of the model, we introduce a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Some numerical illustrations considering simulated and real lifetime data are presented to illustrate the proposed methodology, especially the effects of different priors on the posterior summaries of interest.

14p toritori 11-05-2020 10 1   Download

Qualitative risk assessment methods are often used as the first step to determining design space boundaries; however, quantitative assessments of risk with respect to the design space, i.e., calculating the probability of failure for a given severity, are needed to fully characterize design space boundaries. Quantitative risk assessment methods in design and operational spaces are a significant aid to evaluating proposed design space boundaries.

12p caothientrangnguyen 09-05-2020 16 0   Download

Stochastic Optimality Theory (Boersma, 1997) is a widely-used model in linguistics that did not have a theoretically sound learning method previously. In this paper, a Markov chain Monte-Carlo method is proposed for learning Stochastic OT Grammars. Following a Bayesian framework, the goal is finding the posterior distribution of the grammar given the relative frequencies of input-output pairs. The Data Augmentation algorithm allows one to simulate a joint posterior distribution by iterating two conditional sampling steps. ...

8p bunbo_1 17-04-2013 65 1   Download

Chapter 9 BAYESIAN SYSTEMS ANALYSIS OF SIMULTANEOUS EQUATION This chapter discusses classical estimation methods for limited dependent variable (LDV) models that employ Monte Carlo simulation techniques to overcome computational problems in such models.

82p mama15 30-09-2010 57 8   Download