mcmc

用MCMC做参数估计 来源： https://www.cnblogs.com/shanyr/p/11964354.html

I wanted to know whether it is possible to incorporate Stan in another C++ application. Since Stan is also written in C++, there should be a way. Currently, I am using RInside to achieve this but then you have all this data transferring which is time-consuming. What specifically did you want from Stan? We're going to separate out the math library into a standalone include for Stan 2.7 --- that contains all the matrix, probability, and autodiff code. Our repos already reflect this structure. All of the MCMC and transform and I/O code is callable through C++, as is the translator from a Stan

This question was migrated from Cross Validated because it can be answered on Stack Overflow. Migrated 4 years ago . I've implemented the Bayesian Probabilistic Matrix Factorization algorithm using pymc3 in Python. I also implemented it's precursor, Probabilistic Matrix Factorization (PMF). See my previous question for a reference to the data used here. I'm having trouble drawing MCMC samples using the NUTS sampler. I initialize the model parameters using the MAP from PMF, and the hyperparameters using Gaussian random draws sprinkled around 0. However, I get a PositiveDefiniteError when

I'm running a Bayesian MCMC probit model, and I'm trying to implement it in parallel. I'm getting confusing results about the performance of my machine when comparing parallel to serial. I don't have a lot of experience doing parallel processing, so it is possible I'm not doing it right. I'm using MCMCprobit in the MCMCpack package for the probit model, and for parallel processing I'm using parLapply in the parallel package. Here's my code for the serial run, and the results from system.time : system.time(serial<-MCMCprobit(formula=econ_model,data=mydata,mcmc=10000,burnin=100)) user system

I've recently started learning pymc3 after exclusively using emcee for ages and I'm running into some conceptual problems. I'm practising with Chapter 7 of Hogg's Fitting a model to data . This involves a mcmc fit to a straight line with arbitrary 2d uncertainties. I've accomplished this quite easily in emcee , but pymc is giving me some problems. It essentially boils down to using a multivariate gaussian likelihood. Here is what I have so far. from pymc3 import * import numpy as np import matplotlib.pyplot as plt size = 200 true_intercept = 1 true_slope = 2 true_x = np.linspace(0, 1, size) #

一.蒙特卡罗法的缺陷 通常的蒙特卡罗方法可以模拟生成满足某个分布的随机向量，但是蒙特卡罗方法的缺陷就是难以对高维分布进行模拟。对于高维分布的模拟，最受欢迎的算法当属 马尔科夫链蒙特卡罗算法(MCMC)， 他通过构造一条马尔科夫链来分步生成随机向量来逼近制定的分布，以达到减小运算量的目的。 二.马尔科夫链方法概要 马尔科夫链蒙特卡罗方法的基本思路就是想办法构造一个马尔科夫链，使得其平稳分布是给定的某分布，再逐步生模拟该马尔科夫链产生随机向量序列。其基本思路如下。就像是普通的蒙特卡罗方法本质上依赖于概率论中的大数定理，蒙特卡罗方法的理论支撑是 具有遍历性的马尔科夫链的大数定理 。马尔科夫链蒙特卡罗方法的大体思路如下： (1)给定某个分布$p(x)$, 构造某个马尔科夫链$\lbrace X_{t}\rbrace_{t\in\mathbb{N}}$使得$p$是其平稳分布，且满足一定的特殊条件； (2)从一点$x_{0}$出发，依照马尔科夫链$\lbrace X_{t}\rbrace_{t\in\mathbb{N}}$随机生成向量序列$x_{0},x_{1},...$； (3)蒙特卡罗积分估计：计算$E_{p}(f)\approx\sum_{t=1}^{N}f(x_{t})$ 三.MCMC的数学基础——马尔科夫链的遍历性，大数定理 MCMC为什么可以近似计算积分?