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Toward Practical N2 Monte Carlo: the Marginal Particle Filter
Practical N2 Monte Carlo Marginal Particle Filter
2012/9/19
Sequential Monte Carlo techniques are useful for state estimation in non-linear, non-Gaussian dy-namic models. These methods allow us to ap-proximate the joint posterior distribution using sequential ...
A population Monte Carlo scheme with transformed weights and its application to stochastic kinetic models
Population Monte Carlo importance sampling degeneracy of importance weights stochastic kinetic models
2012/9/18
This paper addresses the problem of Monte Carlo approximation of posterior probability distributions. In particular, we have considered a recently proposed technique known as population Monte Carlo (P...
Guaranteed Conservative Fixed Width Confidence Intervals Via Monte Carlo Sampling
Guaranteed Conservative Fixed Width Confidence Intervals Monte Carlo Sampling
2012/9/17
Monte Carlo methods are used to approximate the means,? of random variablesY, whose distributions are not known explicitly. The key idea is that the
average of a random sample,Y1,...,Yn, tends to 礱sn...
Exact Hamiltonian Monte Carlo for Truncated Multivariate Gaussians
Markov Chain Monte Carlo Hamiltonian Monte Carlo Truncated Multivariate Gaus-sians
2012/9/17
We present a Hamiltonian Monte Carlo algorithm to sample from multivariate Gaussian distri-butions in which the target space is constrained by linear and quadratic inequalities or products thereof. Th...
Adaptive Markov Chain Monte Carlo for Auxiliary Variable Method and Its Application to Parallel Tempering
Adaptive Markov Chain Monte Carlo Auxiliary Variable Method Parallel Tempering Conver-gence
2012/9/19
Auxiliary variable methods such as the Parallel Tempering and the cluster Monte Carlo methods generate samples that follow a target distri-bution by using proposal and auxiliary distributions.In sampl...
Sequential Monte Carlo EM for multivariate probit models
Maximum likelihood Multivariate probit Monte Carlo EM adaptive sequential Monte Carlo
2011/7/19
A Monte Carlo EM algorithm is considered for the maximum likelihood estimation of multivariate probit models.
Generalised linear mixed model analysis via sequential Monte Carlo sampling
generalised additive models longitudinal data analysis nonparametric regression sequential Monte Carlo sampler
2009/9/16
We present a sequential Monte Carlo algorithm for the Bayesian analysis of generalised linear mixed models (GLMMs). These models support a variety of interesting regression-type analyses, but performi...