我使用emcee软件包来确定应该遵循泊松分布的测量数据集的最佳参数。我使用的代码是
def lnL_Poisson(theta,x,y,yerr):
logA,beta = theta
A = 10**logA
model = the Poisson likelihood
return np.sum(model)
def lnprior(theta):
logA,beta = theta
if -5 < logA < 0 and -2 < beta < 4:
return 0.0
return -np.inf
def lnprob_Poisson(theta, x, y, yerr):
lp = lnprior(theta)
if not np.isfinite(lp):
return -np.inf
return lp + lnL_Poisson(theta, x, y, yerr)但是,当运行此代码时,它将返回
ValueError Traceback (most recent call last)
<ipython-input-81-460e20ecdf72> in <module>
1 sampler_2 = emcee.EnsembleSampler(nwalkers, ndim, lnprob_Poisson, args=(x, y_obs, dy))
----> 2 tmp = sampler_2.run_mcmc(pos, 500) #Run the sampler 500 times
3 samples_2 = sampler_2.chain[:, 50:, :].reshape((-1, 2))
4 fig = corner.corner(samples_2, labels=[r"$\log(A)$", r"$\beta$"],quantiles=[0.16, 0.5, 0.84], show_titles=True,label_kwargs=dict(fontsize=15))
~\Anaconda3\lib\site-packages\emcee\ensemble.py in run_mcmc(self, initial_state, nsteps, **kwargs)
382
383 results = None
--> 384 for results in self.sample(initial_state, iterations=nsteps, **kwargs):
385 pass
386
~\Anaconda3\lib\site-packages\emcee\ensemble.py in sample(self, initial_state, log_prob0, rstate0, blobs0, iterations, tune, skip_initial_state_check, thin_by, thin, store, progress)
283 state.blobs = blobs0
284 if state.log_prob is None:
--> 285 state.log_prob, state.blobs = self.compute_log_prob(state.coords)
286 if np.shape(state.log_prob) != (self.nwalkers,):
287 raise ValueError("incompatible input dimensions")
~\Anaconda3\lib\site-packages\emcee\ensemble.py in compute_log_prob(self, coords)
454 # Check for log_prob returning NaN.
455 if np.any(np.isnan(log_prob)):
--> 456 raise ValueError("Probability function returned NaN")
457
458 return log_prob, blob
ValueError: Probability function returned NaN当使用高斯日志的可能性时,代码确实有效。我猜这与某个地方的概率为0,然后除以这个值有关。但是,我不知道如何解决这个问题。有人有这方面的经验吗?
发布于 2022-01-27 12:04:17
检查x数组和y数组的dtype。我也遇到了同样的问题,然后发现我的x数组是float32,而y数组是float64。将x转换为float64后,问题得到了解决。
https://stackoverflow.com/questions/67078445
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