A latent slice sampling algorithm
نویسندگان
چکیده
Motivated by a sampling algorithm for discrete spaces, variation of the slice sampler continuous spaces is introduced. It utilizes latent variables and related to Neal's sampler. The key difference that additional allow sequential stepping out or doubling procedures, which makes basic difficult use in high dimensional problems, be avoided. On other hand, applicable on problems where can all treated single block.
منابع مشابه
Slice sampling covariance hyperparameters of latent Gaussian models
The Gaussian process (GP) is a popular way to specify dependencies between random variables in a probabilistic model. In the Bayesian framework the covariance structure can be specified using unknown hyperparameters. Integrating over these hyperparameters considers different possible explanations for the data when making predictions. This integration is often performed using Markov chain Monte ...
متن کاملSlice Sampling
Markov chain sampling methods that automatically adapt to characteristics of the distribution being sampled can be constructed by exploiting the principle that one can sample from a distribution by sampling uniformly from the region under the plot of its density function. A Markov chain that converges to this uniform distribution can be constructed by alternating uniform sampling in the vertica...
متن کاملParallel multivariate slice sampling
Slice sampling provides an easily implemented method for constructing a Markov chain Monte Carlo (MCMC) algorithm. However, slice sampling has two major drawbacks: (i) it requires repeated evaluation of likelihoods for each update, which can make it impractical when evaluations are expensive or as the number of evaluations grows (geometrically) with the dimension of the slice sampler, and (ii) ...
متن کاملPseudo-Marginal Slice Sampling
Markov chain Monte Carlo (MCMC) methods asymptotically sample from complex probability distributions. The pseudo-marginal MCMC framework only requires an unbiased estimator of the unnormalized probability distribution function to construct a Markov chain. However, the resulting chains are harder to tune to a target distribution than conventional MCMC, and the types of updates available are limi...
متن کاملSlice sampling mixture models
We propose a more efficient version of the slice sampler for Dirichlet process mixture models described by Walker (Commun. Stat., Simul. Comput. 36:45–54, 2007). This new sampler allows for the fitting of infinite mixture models with a wide-range of prior specifications. To illustrate this flexibility we consider priors defined through infinite sequences of independent positive random variables...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2023
ISSN: ['0167-9473', '1872-7352']
DOI: https://doi.org/10.1016/j.csda.2022.107652