emcee的核心是一个名为EnsembleSampler的类,它负责实际的采样过程。下面是一个简单的使用示例: importnumpyasnpfromemceeimportEnsembleSampler# 定义一个简单的目标函数,这里以高斯分布为例deflog_prob(x,mu,sigma):return-np.sum((np.array(x)-mu)**2/(2*sigma**2
功能库强大 emcee库 贝叶斯统计 参数估计 入门指南 安装emcee库 Python环境搭建 pip命令安装 emcee及其依赖项 基本使用方法 EnsembleSampler类 MCMC算法 目标函数log_prob 参数概率计算 创建EnsembleSampler对象 run_mcmc方法 进阶用法 调整EnsembleSampler参数 控制采样过程 设置a参数 自适应步长缩放因子 异常处理 ...
pos=theta_guess+1e-4*np.random.randn(nwalkers, ndim) # run emcee sampler=emcee.EnsembleSampler(nwalkers, ndim, log_posterior, args=(x, y, y_err)) sampler.run_mcmc(pos, 10000, progress=True); 结果如下: 100%|██████████| 10000/10000 [00:05<00:00, 1856.57it/s] 我们应...
importnumpyasnp importemcee deflog_prob(x, ivar): return-0.5* np.sum(ivar * x **2) ndim, nwalkers =5,100 ivar =1./np.random.rand(ndim) p0 = np.random.randn(nwalkers, ndim) sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar]) sampler.run_mcmc(p0,10000) 💡...
importnumpyasnpimportemceedeflog_prob(x,ivar):return-0.5*np.sum(ivar*x**2)ndim,nwalkers=5,100ivar=1./np.random.rand(ndim)p0=np.random.randn(nwalkers,ndim)sampler=emcee.EnsembleSampler(nwalkers,ndim,log_prob,args=[ivar])sampler.run_mcmc(p0,10000) ...
import numpy as np import emcee def log_prob(x, ivar): return -0.5 * np.sum(ivar * x ** 2) ndim, nwalkers = 5, 100 ivar = 1./np.random.rand(ndim) p0 = np.random.randn(nwalkers, ndim) sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar]) sampler.run_mcmc...
importnumpyasnpimportemceedeflog_prob(x, ivar):return-0.5* np.sum(ivar * x **2) ndim, nwalkers =5,100ivar =1./np.random.rand(ndim) p0 = np.random.randn(nwalkers, ndim) sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar]) ...
importnumpyasnpimportemceedeflog_prob(x,ivar):return-0.5*np.sum(ivar*x**2)ndim,nwalkers=5,100ivar=1./np.random.rand(ndim)p0=np.random.randn(nwalkers,ndim)sampler=emcee.EnsembleSampler(nwalkers,ndim,log_prob,args=[ivar])sampler.run_mcmc(p0,10000) ...
问用Python的emcee对Maxwellian曲线进行MCMC采样EN在MCMC(三)MCMC采样和M-H采样中，我们讲到了M-H采样...
The PyDE parameter vector population can be used to initialize the affine-invariant MCMC sampleremceewhen a simple point estimate of the function minimum (or maximum) is not sufficient: de = DiffEvol(lnpost, bounds, npop, maximize=True) de.optimize(ngen) sampler = emcee.EnsembleSampler(npop,...