Mode Jumping Proposals in MCMC

No abstract is available for this paper.

• Publication year

  2001

• Venue

  Scandinavian Journal of Statistics

• Publication date

  2001-03-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1111/1467-9469.00232
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Perfect simulation for sample-based inference

  1999influential reference
• Simulating Normalizing Constants: From Importance Sampling to Bridge Sampling to Path Sampling

  1998cited by this paper
• Posterior Simulation and Bayes Factors in Panel Count Data Models

  1998cited by this paper
• Analysis of multivariate probit models

  1998cited by this paper
• How to Get a Perfectly Random Sample from a Generic Markov Chain and Generate a Random Spanning Tree of a Directed Graph

  1998cited by this paper
• Exact Sampling from a Continuous State Space

  1998cited by this paper
• Finite mixture models for proportions.

  1997cited by this paper
• Exact sampling with coupled Markov chains and applications to statistical mechanics

  1996cited by this paper
• Sampling from multimodal distributions using tempered transitions

  1996cited by this paper
• Annealing Markov chain Monte Carlo with applications to ancestral inference

  1995cited by this paper
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination

  1995cited by this paper
• The analysis of repeated-measures data on schizophrenic reaction times using mixture models.

  1995cited by this paper
• Bayesian Computation and Stochastic Systems

  1995cited by this paper
• Inverse problems in groundwater modeling

  1994cited by this paper
• Bayes inference in regression models with ARMA (p, q) errors

  1994cited by this paper
• Spatial Statistics and Bayesian Computation

  1993cited by this paper
• Simulated tempering: a new Monte Carlo scheme

  1992cited by this paper
• Inference from Iterative Simulation Using Multiple Sequences

  1992cited by this paper
• Markov Chain Monte Carlo Maximum Likelihood

  1991influential reference
• Multigrid Monte Carlo method. Conceptual foundations.

  1989cited by this paper
• Optimum Monte-Carlo sampling using Markov chains

  1973cited by this paper
• Distributions in Statistics: Continuous Multivariate Distributions

  1973cited by this paper
• Monte Carlo Sampling Methods Using Markov Chains and Their Applications

  1970influential reference

• Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks

  2025cites this paper
• Dynamic model updating through reliability-based sequential history matching

  2025cites this paper
• Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature

  2025cites this paper
• Evolutionary variational inference for Bayesian generalized nonlinear models

  2024cites this paper
• Prediction of bridge structure deformation and strain based on dynamic testing and intelligent algorithms

  2024cites this paper
• Ensemble-Based Annealed Importance Sampling

  2024cites this paper
• Symmetry-driven embedding of networks in hyperbolic space

  2024cites this paper
• PhyloGFN: Phylogenetic inference with generative flow networks

  2023cites this paper
• Global Likelihood Sampler for Multimodal Distributions

  2023cites this paper
• Accelerate Langevin Sampling with Birth-Death process and Exploration Component

  2023cites this paper
• Advanced data analysis in inertial confinement fusion and high energy density physics.

  2023cites this paper
• Multimodal sampling via approximate symmetries

  2023cites this paper
• MULTIMODAL SAMPLING VIA APPPROXIMATE SYMMETRIES

  2023cites this paper
• Friction-Identification of Harmonic Drive Joints Based on the MCMC Method

  2022cites this paper
• A subsampling approach for Bayesian model selection

  2022cites this paper
• Sampling using adaptive regenerative processes

  2022cites this paper
• A DATA-DRIVEN DIAGNOSTIC FRAMEWORK FOR SMALL WATERSHEDS BASED ON MARKOV CHAIN-MONTE CARLO

  2021cites this paper
• Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods

  2021cites this paper
• A Bayesian Surveillance Model to Track Variable Rainfall-Runoff Responses for Small Watersheds

  2021cites this paper
• Annealed Leap-Point Sampler for Multimodal Target Distributions

  2021influential citation
• Computational Statistics and Data Science in the Twenty‐First Century

  2021cites this paper
• Hopping between distant basins

  2021cites this paper
• Parallel generalized elliptical slice sampling with adaptive regional pseudo-priors

  2020cites this paper
• Flexible Bayesian Nonlinear Model Configuration

  2020cites this paper
• A Bayesian Binomial Regression Model with Latent Gaussian Processes for Modelling DNA Methylation

  2020cites this paper
• Quasi‐Online Groundwater Model Optimization Under Constraints of Geological Consistency Based on Iterative Importance Sampling

  2020cites this paper
• A Mode-Jumping Algorithm for Bayesian Factor Analysis

  2020cites this paper
• Generating Weighted MAX-2-SAT Instances with Frustrated Loops: an RBM Case Study

  2020influential citation
• Penalised t-walk MCMC

  2020cites this paper
• A MODE-JUMPING ALGORITHM FOR EXPLORATORY FACTOR ANALYSIS WITH CONTINUOUS AND BINARY RESPONSES BY ALBERT MAN

  2019influential citation
• Statistical approaches to viral phylodynamics

  2019cites this paper
• Generalized Elliptical Slice Sampling with Regional Pseudo-priors

  2019influential citation
• The Skipping Sampler: A new approach to sample from complex conditional densities.

  2019cites this paper
• Generating Weighted MAX-2-SAT Instances of Tunable Difficulty with Frustrated Loops

  2019cites this paper
• A Metropolis-class sampler for targets with non-convex support

  2019cites this paper
• Monte Carlo Approximation of Bayes Factors via Mixing With Surrogate Distributions

  2019cites this paper
• Bayesian model configuration, selection and averaging in complex regression contexts

  2018cites this paper
• A framework for adaptive MCMC targeting multimodal distributions

  2018cites this paper
• A multiple-try Metropolis–Hastings algorithm with tailored proposals

  2018cites this paper
• Deep Bayesian regression models

  2018cites this paper
• Weight-preserving simulated tempering

  2018cites this paper
• Bayesian approach to disease model calibration

  2018cites this paper
• Robust visual data segmentation: Sampling from distribution of model parameters

  2018cites this paper
• A Novel Algorithmic Approach to Bayesian Logic Regression (with Discussion)

  2017cites this paper
• A review of multiple try MCMC algorithms for signal processing

  2017cites this paper
• Improved image analysis by maximised statistical use of geometry-shape constraints

  2017cites this paper
• MCMC based sampling technique for robust multi-model fitting and visual data segmentation

  2016cites this paper
• Mode jumping MCMC for Bayesian variable selection in GLMM

  2016influential citation
• A Repelling–Attracting Metropolis Algorithm for Multimodality

  2016cites this paper
• Metropolized Randomized Maximum Likelihood for Improved Sampling from Multimodal Distributions

  2015cites this paper
• On Gibbs sampler and Metrolopolis-Hastings applied to pairwise poisson and car crash data

  2013cites this paper
• Parallel Multi-Proposal and Multi-Chain MCMC for calculating P-Value of genome-Wide Association Study

  2013cites this paper
• Synthesizing open worlds with constraints using locally annealed reversible jump MCMC

  2012cites this paper
• Local Derivative-Free Approximation of Computationally Expensive Posterior Densities

  2012cites this paper
• Uncertainty Analysis for Computationally Expensive Models with Multiple Outputs

  2012cites this paper
• Model choice using reversible jump Markov chain Monte Carlo

  2012cites this paper
• Sparse Approximate Manifolds for Differential Geometric MCMC

  2012cites this paper
• On the Flexibility of Metropolis–Hastings Acceptance Probabilities in Auxiliary Variable Proposal Generation

  2011influential citation
• Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction

  2011cites this paper
• Model Choice using Reversible Jump Markov Chain

  2011cites this paper
• Adaptive independent Metropolis–Hastings

  2009influential citation
• Efficient Algorithms for Fitting Bayesian Mixture Models

  2009influential citation
• New methods for mode jumping in Markov chain Monte Carlo algorithms

  2009influential citation
• Reversible jump MCMC

  2009cites this paper
• Posterior Approximation by Interpolation for Bayesian Inference in Computationally Expensive Statistical Models

  2008cites this paper
• Improving Efficiency in Parameter Estimation Using the Hamiltonian Monte Carlo Algorithm

  2008cites this paper
• Distributed micro-releases of bioterror pathogens: threat characterizations and epidemiology from uncertain patient observables

  2008cites this paper
• Mode jumping in MCMC

  2008influential citation
• Parallel Multiple Proposal MCMC Algorithms

  2007influential citation
• Bayesian analysis of lidar signals using reversible jump Markov chain Monte Carlo algorithms

  2007cites this paper
• Generalized Darting Monte Carlo

  2007cites this paper
• Searching for convergence in phylogenetic Markov chain Monte Carlo.

  2006influential citation
• Markov chain Monte Carlo, recent developments

  2005cites this paper
• Towards Automatic Reversible Jump Markov Chain Monte Carlo

  2005cites this paper
• Bayesian Sampling Algorithms for the Sample Selection and Two-Part Models

  2005cites this paper
• On the use of local optimizations within Metropolis–Hastings updates

  2004influential citation
• Detection of Tracks in Aerial Photos by the Gibbs Sampler

  2003cites this paper
• An approximate alternative to perfect simulation

  2003cites this paper
• A Mode-Hopping MCMC sampler

  2003cites this paper
• Wormholes Improve Contrastive Divergence

  2003cites this paper
• Bayesian calibration of computer models

  2001cites this paper
• Norges Teknisk-naturvitenskapelige Universitet Control Variates for the Metropolis-hastings Algorithm Control Variates for the Metropolis-hastings Algorithm

  year unknowninfluential citation