6533b853fe1ef96bd12ad40b

RESEARCH PRODUCT

MCMC methods to approximate conditional predictive distributions

J. MoralesM. J. BayarriMaria Eugenia Castellanos

subject

Statistics and ProbabilityMarkov chainApplied MathematicsMarkov chain Monte CarloConditional probability distributionBayesian inferenceComputational Mathematicssymbols.namesakeMetropolis–Hastings algorithmComputational Theory and MathematicsSampling distributionFrequentist inferencesymbolsEconometricsAlgorithmMathematicsGibbs sampling

description

Sampling from conditional distributions is a problem often encountered in statistics when inferences are based on conditional distributions which are not of closed-form. Several Markov chain Monte Carlo (MCMC) algorithms to simulate from them are proposed. Potential problems are pointed out and some suitable modifications are suggested. Approximations based on conditioning sets are also explored. The issues are illustrated within a specific statistical tool for Bayesian model checking, and compared in an example. An example in frequentist conditional testing is also given.

yearjournalcountryeditionlanguage
2006-11-01Computational Statistics & Data Analysis
https://doi.org/10.1016/j.csda.2006.01.018