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Adaptive Metropolis-Hastings Sampling using Reversible Dependent Mixture Proposals
Ergodic convergence Markov Chain Monte Carlo Metropolis-within Gibbs composite sampling Multivariatet mixtures Simulated annealing Variational Approx-imation
2013/6/14
This article develops a general-purpose adaptive sampler that approximates the target density by a mixture of multivariate t densities. The adaptive sampler is based on reversible proposal distributio...
Exact recording of Metropolis-Hastings-class Monte Carlo simulations using one bit per sample
Markov chain Monte Carlo Metropolis-Hastings information theory data representation
2011/6/21
The Metropolis-Hastings (MH) algorithm is the prototype for a class of Markov chain Monte Carlo methods
that propose transitions between states and then accept or reject the proposal. These methods g...
Variable-at-a-time Implementations of Metropolis-Hastings
Variable-at-a-time Implementations Metropolis-Hastings
2010/3/18
It is common practice in Markov chain Monte Carlo to update a high-dimensional
chain one variable (or sub-block of variables) at a time, rather than conduct a single block
update. While this modific...