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MCMC and Stan
In Bayesian statistics, it is generally difficult to mathematically derive the posterior distribution, except in special cases. Instead, it is common...
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MCMC and Stan
In Bayesian statistics, it is generally difficult to mathematically derive the posterior distribution, except in special cases. Instead, it is common...
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McMC in Practice
This chapter illustrates applications of McMC in a Bayesian context. The treatment is mostly schematic; the objective is to present the mechanics of...
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Diffusion Approximations and Control Variates for MCMC
AbstractA new method is introduced for the construction of control variates to reduce the variance of additive functionals of Markov Chain Monte...
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Efficient parameter generation for constrained models using MCMC
Mathematical models of complex systems rely on parameter values to produce a desired behavior. As mathematical and computational models increase in...
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Single MCMC chain parallelisation on decision trees
Decision trees (DT) are highly famous in machine learning and usually acquire state-of-the-art performance. Despite that, well-known variants like...
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Exploration of the MCMC Wald test with linear regression
Recently, Asparouhov and Muthén Structural Equation Modeling: A Multidisciplinary Journal , 28 , 1–14, (
2021a ,2021b ) proposed a variant of the Wald...
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MCMC from Scratch A Practical Introduction to Markov Chain Monte Carlo
This textbook explains the fundamentals of Markov Chain Monte Carlo (MCMC) without assuming advanced knowledge of mathematics and programming. MCMC...
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Two-Stage MCMC with Surrogate Models for Efficient Uncertainty Quantification in Multiphase Flow
We present a novel two-stage Markov Chain Monte Carlo (MCMC) method that improves the efficiency of MCMC sampling while maintaining its sampling...
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Rate-optimal refinement strategies for local approximation MCMC
Many Bayesian inference problems involve target distributions whose density functions are computationally expensive to evaluate. Replacing the target...
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Real-Time Tracking of Basketball Trajectory Based on the Associative MCMC Model
In basketball videos, the trajectories of a basketball changes rapidly. Since the visual features changes in a more homogeneous region, the frame...
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MCMC sampling of directed flag complexes with fixed undirected graphs
Constructing null models to test the significance of extracted information is a crucial step in data analysis. In this work, we provide a uniformly...
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An Adaptive, Energy-Efficient DRL-Based and MCMC-Based Caching Strategy for IoT Systems
The Internet of Things (IoT) has seen remarkable growth in recent years, but the data volatility and limited energy resources in these networks pose...
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Approximated Gaussian Random Field Under Different Parameterizations for MCMC
Fitting spatial models with a Gaussian random field as spatial random effect poses computational challenges for Markov Chain Monte Carlo (MCMC)...
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Statistic selection and MCMC for differentially private Bayesian estimation
This paper concerns differentially private Bayesian estimation of the parameters of a population distribution, when a noisy statistic of a sample...
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Convergence of stratified MCMC sampling of non-reversible dynamics
We present a form of stratified MCMC algorithm built with non-reversible stochastic dynamics in mind. It can also be viewed as a generalization of...
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Parallelizing MCMC sampling via space partitioning
Efficient sampling of many-dimensional and multimodal density functions is a task of great interest in many research fields. We describe an algorithm...
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MMG 3DOF model identification with uncertainty of observation and hydrodynamic maneuvering coefficients using MCMC method
The trajectory prediction using ship maneuverability mathematical models is one of the essential technologies implemented in autonomous surface ship....
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MCMC Convergence for Global-Local Shrinkage Priors
Global-local continuous shrinkage priors have emerged as effective and computationally scalable tools for Bayesian sparsity-based regularization in...
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Estimation of Uncertainties in Soil Using MCMC Simulation and Effect of Model Uncertainty
The simulation of field conditions for seismically induced slope failures incorporates model uncertainties, which account for the difference between...
