MCMC og sampling
48 metoder i denne familie.
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Bayesiansk Dynamisk Betinget Korrelations-GARCH (Bayesiansk DCC-GARCH)Bayesian DCC-GARCH estimates time-varying correlations across multiple financial or economic series by combining Engle's DCC-GARCH structure with Bayesian inference. Rather than maBayesiansk Gaussisk Blanding (Bayesian Gaussian Mixture Model)The Bayesian Gaussian Mixture Model places prior distributions over all mixture parameters and infers their posteriors — typically via Variational Bayes or MCMC — rather than fittiBayesiansk Fylogenetisk AnalyseBayesian phylogenetic analysis uses Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling to estimate the posterior probability distribution over phylogenetic trees and modelBayesiansk probitmodelThe Bayesian Probit model is a binary regression method that models the probability of a binary outcome using the normal CDF (probit link) within a Bayesian framework. It assigns pDynamisk Hamiltonsk Monte CarloDynamic Hamiltonian Monte Carlo — widely known as the No-U-Turn Sampler (NUTS) — is an adaptive extension of Hamiltonian Monte Carlo that automatically selects the number of leapfrDynamisk Metropolis-Hastings AlgoritmeThe Dynamic Metropolis-Hastings (Dynamic MH) algorithm applies the Metropolis-Hastings MCMC sampler to Bayesian state-space and time-varying parameter models. At each time step, la
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This topic's most-referenced foundational methods, in the order they were developed — a place to start if you're new here.
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Bayesiansk Dynamisk Betinget Korrelations-GARCH (Bayesiansk DCC-GARCH)Bayesiansk Gaussisk Blanding (Bayesian Gaussian Mixture Model)Bayesiansk Fylogenetisk AnalyseBayesiansk probitmodelDynamisk Hamiltonsk Monte CarloDynamisk Metropolis-Hastings AlgoritmeDynamisk partikelfilterDynamisk Sekventiel Monte CarloGibbs SamplingGibbs Sampling til ModelsammenligningGibbs Sampling med målefejlGibbs Sampling med manglende dataHamiltonian Monte CarloHamiltonian Monte Carlo med målefejlHamiltonian Monte Carlo med manglende dataHierarkisk Hamiltonian Monte CarloHierarkisk Markov Chain Monte CarloHierarkisk partikelfilterMarkov Chain Monte Carlo (MCMC)MCMC til modelsammenligningMCMC med målefejlMCMC med manglende dataMetropolis-Hastings AlgoritmenMetropolis-Hastings til model-sammenligningMetropolis-Hastings med målefejlMetropolis-Hastings med manglende dataMultilevel Gibbs SamplingMultilevel Hamiltonian Monte CarloMultilevel MCMCMultilevel Metropolis-HastingsNo-U-Turn Sampler (NUTS)Partikelfilter (sekventiel Monte Carlo)Partikelfilter med målefejlPartikelfilter med manglende dataRobust Gibbs-samplingRobust Hamiltonsk Monte CarloRobust Markovkæde Monte CarloRobust Particle FilterRobust Sekventiel Monte CarloSekventiel Monte CarloSekventiel Monte Carlo med målefejlSekventiel Monte Carlo med manglende dataSlice SamplingSpatial Gibbs SamplingSpatial MCMCTidsserie MCMCTidsserie partikelfilterSekventiel Monte Carlo for tidsserier