Skip to search formSkip to main contentSkip to account menu

Slice sampling

Slice sampling is a type of Markov chain Monte Carlo algorithm for pseudo-random number sampling, i.e. for drawing random samples from a statistical… 
Wikipedia (opens in a new tab)

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
We propose a novel Bayesian Optimization approach for black-box functions with an environmental variable whose value determines… 
Highly Cited
2014
Highly Cited
2014
A new class of dependent random measures which we call compound random measures is proposed and the use of normalized versions of… 
Highly Cited
2014
Highly Cited
2014
Bayesian optimization is a sample-efficient method for black-box global optimization. How- ever, the performance of a Bayesian… 
2012
2012
This paper presents an efficient Gaussian process inference scheme for modeling shortscale phenomena in spatio-temporal datasets… 
Highly Cited
2012
Highly Cited
2012
First-order probabilistic models combine the power of first-order logic, the de facto tool for handling relational structure… 
Highly Cited
2011
Highly Cited
2011
Recent work has shown that complex lighting effects can be well approximated by gathering the contribution of hundreds of… 
2009
2009
This invention provides gastrointestinal peptides useful as antimicrobial and anti-inflammatory agents. This invention also… 
Highly Cited
2008
Highly Cited
2008
The infinite hidden Markov model is a non-parametric extension of the widely used hidden Markov model. Our paper introduces a new… 
Highly Cited
2005
Highly Cited
2005
An auxiliary variable method based on a slice sampler is shown to provide an attractive simulation-based model fitting strategy…