Results 101 to 110 of about 5,420,390 (236)

On some asymptotic properties of the expectation-maximization algorithm and the Metropolis-Hastings algorithm [PDF]

, 2008
研吾 鎌谷
openalex   +1 more source

Ising Hamiltonians for Constrained Combinatorial Optimization Problems and the Metropolis‐Hastings Warm‐Starting Algorithm [PDF]

, 2023
Huimin Li, Jin‐Min Liang, Zhi‐Xi Wang, Shao‐Ming Fei   +3 more
openalex   +1 more source

AdvNF: Reducing mode collapse in conditional normalising flows using adversarial learning

SciPost Physics
Deep generative models complement Markov-chain-Monte-Carlo methods for efficiently sampling from high-dimensional distributions. Among these methods, explicit generators, such as Normalising Flows (NFs), in combination with the Metropolis Hastings ...
Vikas Kanaujia, Mathias S. Scheurer, Vipul Arora
doaj   +1 more source

Efficient Metropolis-Hastings Robbins-Monro Algorithm for High-Dimensional Diagnostic Classification Models

, 2022
Chen‐Wei Liu
openalex   +2 more sources

Adjustment Cost on Investment and Under-Utilization of Maximum Installed Capacity in South Korean Business Cycle - A Bayesian New Keynensian Model

ECONOMICS
This study examines the effects of adjustment costs on investment and the under-utilization of maximum installed capacity within the South Korea using a New Keynesian business cycle with Bayesian approach.
Al Mamun Tuhin G M, Ehsanullah
doaj   +1 more source

Lattice Gaussian Sampling by Markov Chain Monte Carlo: Bounded Distance Decoding and Trapdoor Sampling [PDF]

, 2018
Sampling from the lattice Gaussian distribution plays an important role in various research fields. In this paper, the Markov chain Monte Carlo (MCMC)-based sampling technique is advanced in several fronts.
Ling, Cong, Wang, Zheng
core   +1 more source

Maximal couplings of the Metropolis-Hastings algorithm [PDF]

, 2020
John R. O’Leary, Guanyang Wang, Pierre Jacob   +2 more
openalex   +1 more source

Group Importance Sampling for Particle Filtering and MCMC

, 2018
Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years.
Camps-Valls, G., Elvira, V., Martino, L.
core  

Adaptive Polar Sampling with an Application to a Bayes Measure of Value-at-Risk [PDF]

Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlo method for Bayesian analysis of models with ill-behaved posterior distributions.
Bauwens, L., Bos, C.S., Dijk, H.K. van
core   +1 more source

The Hybrid parameter learning algorithm development for dynamic Bayesian network in the context of the Metropolis — Hastings approach [PDF]

, 2019
Т В Азарнова, P V Polukhin
openalex   +1 more source