Results 141 to 150 of about 151,127 (170)
<i>Vitess 3.8</i>: a modernized framework for Monte Carlo neutron tracing simulations. [PDF]
J Appl CrystallogrRobledo JI +5 more
europepmc +1 more source
Sequential Monte Carlo samplers [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2006 SummaryWe propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time by using sequential Monte Carlo methods.
Pierre Del Moral +2 more
exaly +4 more sources
An Invitation to Sequential Monte Carlo Samplers
Journal of the American Statistical Association, 2022 37 pages, 8 figures; small typos ...
Chenguang Dai +2 more
exaly +4 more sources
Adaptive Tuning of Hamiltonian Monte Carlo Within Sequential Monte Carlo [PDF]
Bayesian Analysis, 2021 Sequential Monte Carlo (SMC) samplers form an attractive alternative to MCMC for Bayesian computation. However, their performance depends strongly on the Markov kernels used to rejuvenate particles. We discuss how to calibrate automatically (using the current particles) Hamiltonian Monte Carlo kernels within SMC.
Nicolas Chopin, Pierre E Jacob
exaly +4 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
2020
Sequential Monte Carlo (SMC) is used when the distribution of interest is one-dimensional or multi-dimensional and factorizable. If f(x) denotes the true probability distribution function controlling a process and π(x) denotes a target probability distribution based on a model, then the goal is to find a model to make the target density function π(x ...
Adrian Barbu, Song-Chun Zhu
openaire +1 more source
Sequential Monte Carlo (SMC) is used when the distribution of interest is one-dimensional or multi-dimensional and factorizable. If f(x) denotes the true probability distribution function controlling a process and π(x) denotes a target probability distribution based on a model, then the goal is to find a model to make the target density function π(x ...
Adrian Barbu, Song-Chun Zhu
openaire +1 more source
Sequential Monte Carlo simulated annealing
Journal of Global Optimization, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Enlu Zhou, Xi Chen
openaire +2 more sources
Mathematical Proceedings of the Cambridge Philosophical Society, 1962
ABSTRACTThis paper defines the concept of sequential Monte Carlo and outlines the principal modes of approach which may be expected to yield useful sequential processes. Three workable sequential processes, derived from a non-sequential method of J. von Neumann and S. M. Ulam for solving systems of linear algebraic equations, are described and analysed
openaire +2 more sources
ABSTRACTThis paper defines the concept of sequential Monte Carlo and outlines the principal modes of approach which may be expected to yield useful sequential processes. Three workable sequential processes, derived from a non-sequential method of J. von Neumann and S. M. Ulam for solving systems of linear algebraic equations, are described and analysed
openaire +2 more sources
Sequential Monte Carlo Methods in Practice
Technometrics, 2003(2003). Sequential Monte Carlo Methods in Practice. Technometrics: Vol. 45, No. 1, pp. 106-106.
openaire +1 more source
Theory of Sequential Monte Carlo
2004In the previous chapter, we introduced the basic framework of sequential importance sampling (SIS), in which one builds up the trial sampling distribution sequentially and computes the importance weights recursively.
openaire +1 more source