Sampling from Linear Multivariate Densities
نویسندگان
چکیده
It is well known that the generation of random vectors with nonindependent components is difficult. Nevertheless, we propose a new and very simple generation algorithm for multivariate linear densities over point-symmetric domains. Among other applications it can be used to design a simple decompositionrejection algorithm for multivariate concave distributions.
منابع مشابه
Metropolis - Hastings Sampling Using Multivariate Gaussian Tangents
We present MH-MGT, a multivariate technique for sampling from twice-differentiable, log-concave probability density functions. MHMGT is Metropolis-Hastings sampling using asymmetric, multivariate Gaussian proposal functions constructed from Taylor-series expansion of the log-density function. The mean of the Gaussian proposal function represents the full Newton step, and thus MH-MGT is the stoc...
متن کاملMultivariate generalized linear mixed models with semi-nonparametric and smooth nonparametric random effects densities
We extend the family of multivariate generalized linear mixed models to include random effects that are generated by smooth densities. We consider two such families of densities, the so-called semi-nonparametric (SNP) and smooth nonparametric (SMNP) densities. Maximum likelihood estimation, under either the SNP or the SMNP densities, is carried out using a Monte Carlo EM algorithm. This algorit...
متن کاملBayesian Inference for the Multivariate Normal
Bayesian inference for the multivariate Normal is most simply instantiated using a Normal-Wishart prior over the mean and covariance. Predictive densities then correspond to multivariate T distributions, and the moments from the marginal densities are provided analytically or via Monte-Carlo sampling. We show how this textbook approach is applied to a simple two-dimensional example.
متن کاملConvex Chance Constrained Predictive Control without Sampling
In this paper we consider finite-horizon predictive control of dynamic systems subject to stochastic uncertainty; such uncertainty arises due to exogenous disturbances, modeling errors, and sensor noise. Stochastic robustness is typically defined using chance constraints, which require that the probability of state constraints being violated is below a prescribed value. Prior work showed that i...
متن کاملGibbs sampling approach for generation of truncated multivariate Gaussian random variables
In many Monte Carlo simulations, it is important to generate samples from given densities. Recently, researchers in statistical signal processing and related disciplines have shown increased interest for a generator of random vectors with truncated multivariate normal probability density functions (pdf's). A straightforward method for their generation is to draw samples from the multivariate no...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2011