MCMC dan penyampelan
48 metode dalam keluarga ini.
Unggulan
Bayesian DCC-GARCHBayesian DCC-GARCH estimates time-varying correlations across multiple financial or economic series by combining Engle's DCC-GARCH structure with Bayesian inference. Rather than maModel Campuran Gaussian BayesianThe Bayesian Gaussian Mixture Model places prior distributions over all mixture parameters and infers their posteriors — typically via Variational Bayes or MCMC — rather than fittiAnalisis Filogenetik BayesianBayesian phylogenetic analysis uses Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling to estimate the posterior probability distribution over phylogenetic trees and modelModel Probit BayesThe 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 pHamiltonian Monte Carlo DinamisDynamic 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 leapfrAlgoritma Metropolis-Hastings DinamisThe 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
Jalur bacaan
Metode fondasional yang paling banyak dirujuk pada topik ini, dalam urutan pengembangannya — tempat untuk memulai jika Anda baru di sini.
Semua metode 48
Bayesian DCC-GARCHModel Campuran Gaussian BayesianAnalisis Filogenetik BayesianModel Probit BayesHamiltonian Monte Carlo DinamisAlgoritma Metropolis-Hastings DinamisFilter Partikel DinamisMonte Carlo Sekuensial DinamisSampling GibbsSampling Gibbs untuk Perbandingan ModelSampling Gibbs dengan Kesalahan PengukuranSampling Gibbs dengan Data HilangHamiltonian Monte CarloMetode Monte Carlo Hamiltonian dengan Galat PengukuranMetode Monte Carlo Hamiltonian dengan Data HilangHamiltonian Monte Carlo HirarkisMetropolis-Hastings Markov Rantai HirarkisFilter Partikel HirarkisMarkov Chain Monte Carlo (MCMC)MCMC untuk Perbandingan ModelMCMC dengan Kesalahan PengukuranMCMC dengan Data HilangAlgoritma Metropolis-HastingsMetropolis-Hastings untuk Perbandingan ModelMetropolis-Hastings dengan Kesalahan PengukuranMetropolis-Hastings dengan Data HilangSampling Gibbs MultilevelHamiltonian Monte Carlo MultilevelMCMC MultitingkatMetropolis-Hastings MultilevelNo-U-Turn Sampler (NUTS)Filter Partikel (Monte Carlo Sekuensial)Filter Partikel dengan Galat PengukuranFilter Partikel dengan Data HilangSampling Gibbs RobustHamiltonian Monte Carlo RobustMarkov Chain Monte Carlo RobustRobust Particle FilterRobust Sequential Monte CarloMonte Carlo SekuensialMonte Carlo Sekuensial dengan Galat PengukuranSequential Monte Carlo dengan Data HilangSlice SamplingSampling Gibbs SpasialMCMC SpasialMCMC Deret WaktuFilter Partikel Deret WaktuTime Series Sequential Monte Carlo