tau = sampler.get_autocorr_time()print("Autocorrelation time:", tau)mcmc_samples = sampler.get_chain(discard=300, thin=np.int32(np.max(tau)/2), flat=True)print("Remaining samples:", mcmc_samples.shape)#结果Autocorrelation time: [122.51626866 75.87228105 137.195509 54.63572513 79.0331587 ]Remain...
tau = sampler.get_autocorr_time() print("Autocorrelation time:", tau) mcmc_samples = sampler.get_chain(discard=300, thin=np.int32(np.max(tau)/2), flat=True) print("Remaining samples:", mcmc_samples.shape) #结果 Autocorrelation time: [122.51626866 75.87228105 137.195509 54.63572513 79.0331587...
左图的采样样本比右图更聚集 (clumpy),聚集度高的采样链,相邻样本间的相关性高 我们可以通过计算采样序列的自相关系数 (Autocorrelation),来衡量采样链的聚集度 自相关 (Autocorrelation) Lag=1:采样链(X)及该链向前平移了1步(O)叠加的结果 从图上可以观察得到,Lag=1的自相关性比Lag=5,Lag=10更高 自相关函...
print("Autocorrelation time:", tau) mcmc_samples=sampler.get_chain(discard=300, thin=np.int32(np.max(tau)/2), flat=True) print("Remaining samples:", mcmc_samples.shape) #结果 Autocorrelationtime: [122.51626866 75.87228105137.195509 54.63572513 79.0331587 ] Remainingsamples: (4260, 5) 1. 2. ...
#结果Autocorrelationtime: [122.51626866 75.87228105 137.195509 54.63572513 79.0331587 ]Remainingsamples: (4260, 5) emcee 的创建者 Dan Foreman-Mackey 还提供了这一有用的包corner来可视化样本: importcornercorner.corner(mcmc_samples, labels=labels, truths=theta)...
print("Autocorrelation time:", tau) mcmc_samples = sampler.get_chain(discard=300, thin=np.int32(np.max(tau)/2), flat=True) print("Remaining samples:", mcmc_samples.shape) #结果 Autocorrelationtime: [122.51626866 75.87228105 137.195509 54.63572513 79.0331587 ] ...
5.效率和相关性 (Efficiency and Autocorrelation):在实际的MCMC中,连续的样本通常是高度相关的。因此,我们经常需要使用“thinning”(间隔抽样)来减少样本间的相关性。同样,这在示例中也没有提及。 6.初始化和燃烧期 (Initialization and Burn-in):MCMC通常需要一段时间才能开始给出有代表性的样本。这个初期通常被称...
代码语言:javascript 代码运行次数:0 运行 AI代码解释 tau=sampler.get_autocorr_time()print("Autocorrelation time:",tau)mcmc_samples=sampler.get_chain(discard=300,thin=np.int32(np.max(tau)/2),flat=True)print("Remaining samples:",mcmc_samples...
tau=sampler.get_autocorr_time()print("Autocorrelation time:",tau)mcmc_samples=sampler.get_chain(discard=300,thin=np.int32(np.max(tau)/2),flat=True)print("Remaining samples:",mcmc_samples.shape)#结果Autocorrelationtime: [122.5162686675.87228105137.19550954.6357251379.0331587]Remainingsamples: (4260,...
mcmcacf - to plot autocorrelations mcmcgr - Gelman-Rubin R statistic for convergence mcmcdemo - short demonstration program 3. Other ltvec - convert a lower-triangular matrix into a vector veclt - convert a vector into a lower-triangular matrix ...