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Updated: Jan 2, 2026
12:39
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Published on: December 10, 2012
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Posterior-based proposals for speeding up Markov chain Monte Carlo.
C M Pooley1,2, S C Bishop1, A Doeschl-Wilson1
1The Roslin Institute, The University of Edinburgh, Midlothian EH25 9RG, UK.
Royal Society Open Science
|December 13, 2019
Summary
Markov chain Monte Carlo (MCMC) methods struggle with complex models. This study introduces posterior-based proposals (PBPs), a novel MCMC update improving efficiency and speed across diverse applications.
Area of Science:
- Computational Statistics
- Bayesian Inference
- Statistical Modeling
Background:
- Markov chain Monte Carlo (MCMC) is crucial for Bayesian inference in complex systems.
- Poor mixing in MCMC hinders performance with many latent variables, requiring specialized solutions.
Purpose of the Study:
- Introduce posterior-based proposals (PBPs) as a new MCMC update method.
- Address limitations of existing MCMC techniques in handling complex models.
Main Methods:
- Developed posterior-based proposals (PBPs) for MCMC updates.
- PBPs are applicable to statistical models with directed acyclic graph dependencies.
- Generated large joint updates in parameter and latent variable space with high acceptance rates (approx. 33%).
Main Results:
- Evaluated PBPs against standard Gibbs, random walk, Hamiltonian, and particle MCMC methods.
- Tested PBPs on diverse models: disease diagnostics, financial volatility, genetics, and ecology.
- PBPs demonstrated significant speed improvements, up to 10x faster than competing methods in various scenarios.
Conclusions:
- Posterior-based proposals (PBPs) offer a general-purpose, efficient MCMC technique.
- PBPs enhance Bayesian inference performance in complex statistical models.
- This method provides a valuable addition to the MCMC toolkit for diverse applications.
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