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我正在使用 emcee mcmc 锤子从 20 个具有随机标准偏差的样本中重建一维高斯。这是我的代码的相关部分：

def loglike(alpha,datapoints):
mu, sig = alpha
return nbabies*(np.log(1/(nsamples*np.sqrt(2*np.pi)*sig)))+np.sum(scipy.misc.logsumexp(-(datapoints-mu)**2/(2*sig**2),axis=1),axis=0)

import scipy.optimize as op
nll = lambda *args: -loglike(*args)
result = op.minimize(nll, [mupop, sigpop], args=(datapoints))
muml,sigml = result["x"]

def logprior(alpha):
   mu, sig = alpha
    if 0 < sig < 1 and 0.0 < mu < 1:
      return 0.0
return -np.inf

def logprob(alpha,datapoints):
   lp = logprior(alpha)
    if not np.isfinite(lp):
      return -np.inf
return lp + loglike(alpha,datapoints)

rendim = 2
renwalkers = 100
rensamples = 10000

p0 = [result["x"]+np.random.rand(rendim) for i in range(renwalkers)]

#now we run emcee!
momma2 = emcee.EnsembleSampler(renwalkers, rendim, logprob, args=(datapoints))
momma2.run_mcmc(p0, rensamples)
momma2samps = momma2.flatchain[0.2*renwalkers*rensamples:,]

但我不断收到错误消息“ValueError：操作数无法与形状 (1,2) (20,100) 一起广播。” 这是怎么回事？

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1 回答 1

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你的问题是这条线

result = op.minimize(nll, [mupop, sigpop], args=(datapoints))

应该

result = op.minimize(nll, [mupop, sigpop], args=(datapoints,))

minimize将元组作为参数，如果不包含逗号，则无法正确解释参数。

于 2014-07-11T19:55:23.650 回答