Results 41 to 50 of about 167,396 (260)
Orthogonal parallel MCMC methods for sampling and optimization
, 2016 Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration
Corander, J. +4 more
core +2 more sources
Digital Forensics Report Management System for MCMC (DFRMS-MCMC)
, 2023 Malaysian Communication and Multimedia Commission (MCMC) uses a physical report storage system and a stand-alone management system. Both systems have a flaw because workers must come to the office to use it. Therefore, this Digital Forensics Report Case Management System is proposed to help solving those problems.
Zulkifli, Nordiana +4 more
openaire +1 more source
Nuevo Itinerario, 2021 The article that we present here is part of a research / extension process that lasted for more than two years in the Puna and Chaco of Salta, in northern Argentina, with significant interaction with the Kolla de Hurcuro and Wichí de El peoples.
Facundo Gonzalez
doaj +1 more source
Adaptive Metropolis-coupled MCMC for BEAST 2 [PDF]
PeerJ, 2020 With ever more complex models used to study evolutionary patterns, approaches that facilitate efficient inference under such models are needed. Metropolis-coupled Markov chain Monte Carlo (MCMC) has long been used to speed up phylogenetic analyses and to
Nicola F. Müller, Remco R. Bouckaert
doaj +2 more sources
Speeding Up MCMC by Efficient Data Subsampling
, 2018 We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework where the likelihood function for $n$ observations is estimated from a random subset of $m$ observations.
Kohn, Robert +3 more
core +2 more sources
A Classical and Bayesian Approach for Parameter Estimation in Structural Equation Models
Journal of New Theory, 2020 Structural Equation Models (SEMs) with latent variables provide a general framework for modelling relationships in multivariate data. Although SEMs are most commonly used in studies involving intrinsically latent variables, such as happiness, quality of ...
Naci Murat, Mehmet Ali Cengiz
doaj
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models
, 2019 This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of unnormalized probability densities for which the negative log density (or ...
Han, Tian +4 more
core +1 more source
BarekengThis study compares the Log-linear Realized GARCH (LRG) and its extension with Continuous and Jump components (LRG-CJ) in modeling the volatility of financial assets, using daily data from the Tokyo Stock Price Index (TOPIX) over 2004–2011.
Didit Budi Nugroho +2 more
doaj +1 more source
Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations
, 2019 Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models.
Bansal, Prateek +4 more
core +1 more source
SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics
Advanced Science, EarlyView.ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao +11 more
wiley +1 more source