A Bayesian Approach in Estimating Transition Probabilities of a Discrete-time Markov Chain for Ignorable Intermittent Missing Data

DOI10.1080/03610918.2014.911895zbMath1419.62404OpenAlexW1969346551MaRDI QIDQ2821027

Wenyaw Chan, Junsheng Ma, Rachelle S. Doody, Xiaoying Yu, Elaine Symanski

Publication date: 16 September 2016

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2014.911895

zbMATH Keywords

EM algorithmMarkov chainrejection algorithmmissing dataBayesian

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Discrete-time Markov processes on general state spaces (60J05) Diagnostics, and linear inference and regression (62J20)

Cites Work

• Unnamed Item
• Estimating discrete Markov models from various incomplete data schemes
• Another look at rejection sampling through importance sampling
• Statistical Inference about Markov Chains
• A Bayesian Model for Markov Chains via Jeffrey's Prior
• Stochastic Algorithms for Markov Models Estimation with Intermittent Missing Data
• Estimating Transition Probabilities for Ignorable Intermittent Missing Data in a Discrete-Time Markov Chain
• Parameterization and Bayesian Modeling

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