Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition

Pharmacokinetics in Clinical Oncology

Rosner, G. L., Müller, P., Lunagómez, S., & Thompson, P. (2012) · Chapman & Hall/CRC eBooks · doi:10.1201/b11800-5 · Cited by 1

Bayesian non-parametric quantile regression using splines for modelling wave heights

Thompson, P. A., Reeve, D. E., Stander, J., Cai, Y., & Moyeed, R. A. (2008) · CRC Press eBooks · doi:10.1201/9780203883020.ch123 · Cited by 1

The validity and consistency of continuous joystick response in perceptual decision-makingPreprint

Szul, M. J., Bompas, A., Sumner, P., & Zhang, J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/501536 · Cited by 1

Does Social Capital Matter for European Regional Growth?Preprint

Palomino, J. P., & Forte, A. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2222737 · Cited by 1

General Regression Models

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_9 · Cited by 1

Discussion of “Specifying prior distributions in reliability applications”

Ng, H. K. T. (2023) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.2784 · Cited by 1

Bayesian Inference: Theory, Methods, and Computations

Kakamu, K., & Liu, S. (2025) · Technometrics · doi:10.1080/00401706.2025.2565974 · Cited by 1

Bayesian methods for inverse problems in signal and image processing

Marnissi, Y. (2017) · doi:10.70675/c8964ad0zabafz4951zb28fz4906b5e85167 · Cited by 1

Learning with reproducing kernel Hilbert spaces : stochastic gradient descent and laplacian estimation

Pillaud‐Vivien, L. (2020) · theses.fr (ABES) · doi:10.70675/f938eaabz37fcz4483zba85z57f0849e7747 · Cited by 1

Bayesian Inference for Diffusions with Low-Frequency Observations

Fuchs, C. (2012) · Springer eBooks · doi:10.1007/978-3-642-25969-2_7

Some Linear Algebra

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_15

The Role of Within-Trip Dynamics and Shopper Purchase History on Unplanned Purchase BehaviorPreprint

Gilbride, T. J., Inman, J. J., & Stilley, K. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2439950

Preliminaries for Nonparametric Regression

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_10

Data Mining for Longitudinal Data with Different Treatments

Akacha, M., Fonseca, T. C. O., & Liverani, S. (2011) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-642-11363-5_46

Stochastische Prozesse und Bayessches Schätzen

Benner, P., & Poppe, S. (2014) · Springer eBooks · doi:10.1007/978-3-658-01164-2_10

S.-Y. Lee (2007) Structural Equation Modeling, A Bayesian Approach. West Sussex: Wiley. 432+xv pages. US$130.00. ISBN: 978-0470024232

Palomo, J. (2009) · Psychometrika · doi:10.1007/s11336-009-9128-x

Some Counting Processes and Ozone Air Pollution

Rodrigues, E. R., & Achcar, J. A. (2012) · SpringerBriefs in mathematics · doi:10.1007/978-1-4614-4645-3_5

On the Tracking and Replication of Hedge Fund Optimal Investment Portfolio Strategies in Global Capital Markets in Presence of Nonlinearities, Applying Bayesian Filters: 1. Stratanovich – Kalman – Bucy Filters for Gaussian Linear Investment Returns Distribution and 2. Particle Filters for Non-Gaussian Non-Linear Investment Returns DistributionPreprint

Ledenyov, D. O., & Ledenyov, V. O. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2588380

Discussion on “Quickest Detection Problems: Fifty Years Later” by Albert N. Shiryaev

Mukhopadhyay, N. (2010) · Sequential Analysis · doi:10.1080/07474946.2010.520612

A Direct Bayesian Estimation of Reliability

Okamoto, Y. (2013) · Behaviormetrika · doi:10.2333/bhmk.40.149

Setwise and filtered gibbs samplers for teletraffic analysis

Andrew, L. L. H., Qian, G., & Vázquez-Abad, F. J. (2010) · ACM Transactions on Modeling and Computer Simulation · doi:10.1145/1734222.1734223

Bayesian Temporal Modeling of Water Demands at Household Level

Arandia-Perez, E., Uber, J. G., Boccelli, D. L., Janke, R., Hartman, D., & Lee, Y.-H. (2011) · Water Distribution Systems Analysis Conference (WDSA) · doi:10.1061/41203(425)110

Functions of Normal Random Variables

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_17

Bound estimation of multistage asset allocation based on MCMC

Zhang, X., & Li, C. (2008) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2008.4620582

B ayesian Aggregation of Experts' Forecasts

Lichtendahl, K. C. (2011) · Wiley Encyclopedia of Operations Research and Management Science · doi:10.1002/9780470400531.eorms0098

Surrogate ranking for very expensive similarity queries

Xu, F., Jampani, R., Wu, M., Jermaine, C., & Kahveci, T. (2010) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2010.5447888

General Regression Models

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_6

Estimation of Failure Probability by Limit State Sampling

Depina, I., Eiksund, G., Le, T. M. H., & Fenton, G. A. (2014) · Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA) · doi:10.1061/9780784413609.207

A State-Space Model Approach to the Return DecompositionPreprint

Chang, Y.-S., & Savickas, R. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2170586

Basic Large Sample Theory

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_19

Markov Chain Monte Carlo Analysis of Trophic Cascade in Yellowstone after Reintroduction of Wolves

Johnson, D., Klinke, D. J., Wang, Q., Condon, M., & Wang, Z. (2015) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-319-12307-3_36

Bibliography

Shi, J. Q., & Choi, T. (2011) · Chapman & Hall/CRC eBooks · doi:10.1201/b11038-11

Bayesian inference for the bivariate extreme model

Amin, N. A. M., & Adam, M. B. (2016) · AIP conference proceedings · doi:10.1063/1.4965217

Uncertainty quantification in power system reliability using a Bayesian framework

Xu, M., Dent, C. J., & Wilson, A. L. (2016) · International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) · doi:10.1109/pmaps.2016.7764187

Latent Topic-Semantic Indexing Based Automatic Text Summarization

Yu, J., & Chen, X. (2016) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2016.0028

A Dynamic BI–Orthogonal Field Equation Approach to Efficient Bayesian Inversion

Tagade, P., & Han-lim Choi, A. (2017) · International Journal of Applied Mathematics and Computer Science · doi:10.1515/amcs-2017-0016

Stochastic Filtering Methods in Electronic Trading

Bilokon, P., Gwinnutt, J., & Jones, D. (2017) · Mathematics in industry · doi:10.1007/978-3-319-61282-9_28

A Bayesian approach to extended models for exceedance

do Nascimento, F. F., & Bourguignon, M. (2017) · Brazilian Journal of Probability and Statistics · doi:10.1214/17-bjps378

Bayesian modeling of temperature-related mortality with latent functional relationships

Aykroyd, R. G. (2018) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2017.1421223

Asymptotically Independent Samplers

Martino, L., Luengo, D., & Mı́guez, J. (2018) · Statisctics and computing/Statistics and computing · doi:10.1007/978-3-319-72634-2_7

Stochastic Modelling of Biochemical Networks and Inference of Model Parameters

Purutçuoğlu, V. (2018) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-319-74086-7_18

Stock Market Contagion from a Spatial Perspective

Chow, W. W. (2018) · International symposia in economic theory and econometrics · doi:10.1108/s1571-038620180000025005

Compact I/O-Efficient Representation of Separable Graphs and Optimal Tree Layouts

Gavenčiak, T., & Tětek, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-14812-6_14

Bayesian Analysis

(2012) · Wiley series in probability and statistics · doi:10.1002/9780470975916.ch2

Do Maternal Health Problems Influence Child's Worrying Status? Evidence from British Cohort StudyPreprint

Dai, X., Härdle, W. K., & Yu, K. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2892623

Actuarial Applications and Estimation of Extended CreditRisk+

Hirz, J., Schmock, U., & Shevchenko, P. V. (2017) · Risks · doi:10.3390/risks5020023

Métodos de Monte Carlo Hamiltoniano aplicados em modelos GARCH

Xavier, C. (2019) · doi:10.11606/t.104.2019.tde-09102019-145123

Spatiotemporal point processes: regression, model specifications and future directions

Gamerman, D. (2019) · Brazilian Journal of Probability and Statistics · doi:10.1214/19-bjps444

Power Analysis, p Values, and Bayesian Techniques: How Bayesian Techniques Can Be Used in HRD Literature

Baker, R. M., & Hirudayaraj, M. (2019) · Advances in Developing Human Resources · doi:10.1177/1523422319870565 · Cited by 1

Gradient Profile Estimation Using Exponential Cubic Spline Smoothing in a Bayesian Framework

Silva, K. D., Cafaro, C., & Giffin, A. (2021) · Entropy · doi:10.3390/e23060674

Analysis of Diagnostic Signs of Defective States of Mechatronic Mechanisms of Cyclic Action

Константинович, А. А., Игоревич, Ф. Г., Анатольевич, Г. В., & Kovaleva, N. L. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-39216-1_20

Uncertainty quantification in reservoirs with faults using a sequential approach

Estes, S., & Dawson, C. N. (2021) · Computational Geosciences · doi:10.1007/s10596-020-10021-2

Evaluation of the financial condition of companies after the announcement of arrangement bankruptcy: application of the classical and Bayesian logistic regression

Pawełek, B., Kostrzewska, J., Kostrzewski, M., & Gałuszka, K. (2020) · Przegląd Statystyczny Statistical Review · doi:10.5604/01.3001.0014.1782

Statistical Distribution Component Decomposition Method for Manufacturing Quality Control by Using Variational Inference Method

Tamaki, K. (2020) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon43393.2020.9255173

Higher Minimum Quality Standards and Redistributive Effects on Consumer WelfarePreprint

Kotschedoff, M. J. W., & Pachali, M. J. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3214249

Parameter estimation for correlated Ornstein-Uhlenbeck time-seriesPreprint

Strey, H. H., Kumar, R., & Mujica-Parodi, L. R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.12.430978

Cash Flows Discounted Using a Model-Free SDF Extracted under a Yield Curve Prior

Gallant, A. R., & Tauchen, G. E. (2021) · Journal of risk and financial management · doi:10.3390/jrfm14030100

G ibbs Sampling

Christen, J. A. (2016) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat00211.pub2

Bayesian meta-elliptical multivariate regression models with fixed marginals on unit intervals

Rodrigues, J., Benites, Y. R., Cancho, V. G., Balakrishnan, N., & Suzuki, A. K. (2021) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2021.1933531

Reversible Jump MCMC for Deghosting in MSPSR Systems

Kulmon, P. (2021) · Sensors · doi:10.3390/s21144815

Spline and Kernel Methods

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_11

Model Specification and Model Performance

Triantafyllopoulos, K. (2021) · Springer texts in statistics · doi:10.1007/978-3-030-76124-0_4

Matrix Algebra, Probability and Statistics

Triantafyllopoulos, K. (2021) · Springer texts in statistics · doi:10.1007/978-3-030-76124-0_2

Effective sample size approximations as entropy measures

Martino, L., & Elvira, V. (2025) · Computational Statistics · doi:10.1007/s00180-025-01665-8

Tobit modeline bayesci yaklaşım ve winBUGS ile uygulama

Samet, K. A. Y. A., & Babacan, E. K. (2021) · Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.17714/gumusfenbil.873998

Antithrombotic treatment following coronary artery bypass surgery: a network meta-analysis

Abbasciano, R. G., Gradinariu, G., Kourliouros, A., Lai, F., Langrish, J. P., Murphy, G. J., Quarto, C., Radhakrishnan, A., Raja, S. G., Rogers, L., et al. (2021) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd014817

References

Jackman, S. D. (2009) · Wiley series in probability and statistics · doi:10.1002/9780470686621.refs

Matrix Results

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_14

Bayesian Information Fusion for Imprecise Probabilistic Models with Different Types of Information

Wang, C., Yang, L., & Rocchetta, R. (2021) · International Conference on System Reliability and Safety Engineering (SRSE) · doi:10.1109/srse54209.2021.00066

Bibliography

Taroni, F., Bozza, S., Biedermann, A., Garbolino, P., & Aitken, C. G. G. (2010) · doi:10.1002/9780470665084.biblio

References

(2011) · Wiley eBooks · doi:10.1002/9781119952954.refs

Computational Intelligence in Energy Generation

(2020) · Advances in civil and industrial engineering book series · doi:10.4018/978-1-7998-1710-9.ch001

Technical Program in full - Part II (RC 1 - VSP P1)

(2016) · Expanded abstracts with biographies/SEG technical program expanded abstracts · doi:10.1190/segam2016-full2

Seismic Inversion Complete Session

(2016) · Expanded abstracts with biographies/SEG technical program expanded abstracts · doi:10.1190/segam2016-si

Linear Models

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_5

Linear Models

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_8

Bayesian analysis in practice

Baio, G. (2012) · Chapman & Hall/CRC eBooks · doi:10.1201/b13099-7

Bayesian Inference

Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_3

An alternative class of models to position social network groups in latent spaces

Nolau, I., & Ferreira, G. D. S. (2022) · Brazilian Journal of Probability and Statistics · doi:10.1214/21-bjps526

The Future of Correlation Modeling

Meissner, G. A. (2013) · doi:10.1002/9781118809204.ch13

References

Sekerke, M. (2015) · doi:10.1002/9781118864784.refs

Blowin' in the Wind

Hirst, B., Jonathan, P., & Randell, D. (2013) · Significance · doi:10.1111/j.1740-9713.2013.00660.x

Improved Radiation Dosimetry/Risk Estimates to Facilitate Environmental Management of Plutonium-Contaminated Sites

Scott, B. R., Tokarskaya, Z. B., Zhuntova, G. V., Osovets, S. V., & A, B. A. B. (2007) · doi:10.2172/923839

B ayesian Statistics in Quantitative Risk Assessment

Congdon, P. D. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat03597

Introduction

Miyawaki, K. (2019) · SpringerBriefs in statistics · doi:10.1007/978-981-15-1857-7_1

Random Number Generation II

(2004) · Statistics, a series of textbooks and monographs · doi:10.1201/9780203022023.ch3

Background for Markov Chain Monte Carlo

Shemyakin, A. E., & Kniazev, A. (2017) · doi:10.1002/9781118959046.ch3

Statistical Inference

Bithell, J. F. (2013) · Springer eBooks · doi:10.1007/978-0-387-09834-0_54

Bayesian Estimation

(2004) · Statistics, a series of textbooks and monographs · doi:10.1201/9780203022023.pt2

Evolução do value relevance: uma abordagem Bayesiana

Costa, T. (2022) · doi:10.14393/ufu.te.2022.185

Modelación de datos composicionales vía mezclas de distribuciones Gamma multivariadas

Gordillo, M. P. R. (2020) · doi:10.24275/uami.sf268537s

Short Comment on amt-2022-181 - effect of optical airmass

(2022) · Atmospheric measurement techniques · doi:10.5194/amt-2022-181-cc1

Comment on amt-2022-181

(2022) · Atmospheric measurement techniques · doi:10.5194/amt-2022-181-rc1

Comment on amt-2022-181

(2022) · Atmospheric measurement techniques · doi:10.5194/amt-2022-181-rc2

Bayesian Computation in a Birnbaum–Saunders Reliability Model with Applications to Fatigue Data

Leiva, V., Ruggeri, F., & Laniado, H. (2021) · Emerging topics in statistics and biostatistics · doi:10.1007/978-3-030-88658-5_3

Applications of Laplace’s method in Bayesian analysis and related topics

Miyata, Y. (2022) · Sugaku Expositions · doi:10.1090/suga/472

Health Line Saúde24: An Econometric Spatial Analysis of Its Use

Simões, P., Natário, I., Carvalho, M. L., Aleixo, S. M., & Gomes, S. (2021) · Springer eBooks · doi:10.1007/978-3-030-71377-5_6

Reply on CC1

Karanikolas, A. (2022) · Atmospheric measurement techniques · doi:10.5194/amt-2022-181-ac1

Reply on RC1

Karanikolas, A. (2022) · Atmospheric measurement techniques · doi:10.5194/amt-2022-181-ac2

Reply on RC2

Karanikolas, A. (2022) · Atmospheric measurement techniques · doi:10.5194/amt-2022-181-ac3

Overview of Markov Chain Monte Carlo for Statistical Inference and its Application

Tao, B., & Chai, C. T. (2002) · Physica-Verlag eBooks · doi:10.1007/978-3-7908-1782-9_49

Markov Chain Monte Carlo - Innovations and Applications

Kendall, W. S., Liang, F., & Wang, J.-S. (2005) · Lecture notes series, Institute For Mathematical Sciences · doi:10.1142/9789812700919 · Cited by 34

Markov Chain Monte Carlo Methods

(2007) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420010718-13

Seeking Application of Markov Chain Monte Carlo in Different Fields

Chen, B. (2025) · Theoretical and Natural Science · doi:10.54254/2753-8818/2025.22222

Markov chain Monte Carlo methods for statistical inference

Besag, J. E. (2004)