An Introduction to Markov Chain Monte Carlo

325 citations recorded by the source corpus.

Citations in the corpus

Bayesian forecasting in economics and finance: A modern review (2022, unknown): “Given the satisfaction of the required convergence conditions (Geyer, 2011), draws θ(i), i = 1, 2, ...,M , produced via iterative sampling from the full conditionals converge in distribution to p(θ|y1:T ) as M → ∞, and can be used to produce a√ M -consistent estimate of the ordinates of p(yT+1|y1:T…”
Measuring the reliability of MCMC inference with bidirectional Monte Carlo (2016, unknown): “(See e.g. [Gey11].)”
Hierarchical Stochastic Neighbor Embedding (2016, unknown): “Landmarks in L s + 1 are selected by sampling the equilibrium distribution π , that is computed using a simple Markov Chain Monte Carlo technique [Gey11].”
Stan: A Probabilistic Programming Language (2017, unknown): “Geyer (2011) provides a thorough introduction to eﬀective sample size and MCSE estimation.”
A unified framework for multilevel uncertainty quantification in Bayesian inverse problems (2016, unknown): “Gelman-Rubin diagnostics for multiple over-dispersed chains [83, 84], we remark that from a pessimistic point of view any convergence diagnostic is heuristics [85].”