MCMC与抽样
48 种方法属于此方法族。
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贝叶斯动态条件相关GARCH (Bayesian 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 ma贝叶斯高斯混合模型The Bayesian Gaussian Mixture Model places prior distributions over all mixture parameters and infers their posteriors — typically via Variational Bayes or MCMC — rather than fitti贝叶斯系统发育分析Bayesian phylogenetic analysis uses Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling to estimate the posterior probability distribution over phylogenetic trees and model贝叶斯概率模型The 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 p动态哈密顿蒙特卡洛Dynamic 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 leapfr动态 Metropolis-Hastings 算法The 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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贝叶斯动态条件相关GARCH (Bayesian DCC-GARCH)贝叶斯高斯混合模型贝叶斯系统发育分析贝叶斯概率模型动态哈密顿蒙特卡洛动态 Metropolis-Hastings 算法动态粒子滤波器动态序贯蒙特卡洛法Gibbs Sampling用于模型比较的吉布斯抽样含测量误差的Gibbs采样带缺失数据的吉布斯抽样Hamiltonian Monte CarloHamiltonian Monte Carlo with Measurement ErrorHamiltonian Monte Carlo with Missing Data分层哈密顿蒙特卡洛分层马尔可夫链蒙特卡洛分层粒子滤波器马尔可夫链蒙特卡洛 (MCMC)用于模型比较的MCMC含测量误差的MCMC缺失数据下的MCMCMetropolis-Hastings算法Metropolis-Hastings 用于模型比较带测量误差的Metropolis-Hastings算法带缺失数据的Metropolis-Hastings算法多层吉布斯采样多层哈密顿蒙特卡洛 (Multilevel Hamiltonian Monte Carlo)多层级 MCMC多层 Metropolis-Hastings无掉头采样器 (NUTS)粒子滤波器（序贯蒙特卡洛）带测量误差的粒子滤波器带缺失数据的粒子滤波器鲁棒吉布斯采样鲁棒哈密顿蒙特卡洛稳健马尔可夫链蒙特卡洛 (Robust Markov Chain Monte Carlo)稳健粒子滤波器鲁棒序贯蒙特卡洛顺序蒙特卡洛带测量误差的序贯蒙特卡洛缺失数据的序贯蒙特卡洛方法切片采样空间吉布斯采样空间马尔可夫链蒙特卡洛 (Spatial MCMC)时间序列 MCMC时间序列粒子滤波器时间序列序列蒙特卡洛方法