问题描述
我正在使用 emcee 包来确定应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,yerr):
lp = lnprior(theta)
if not np.isfinite(lp):
return -np.inf
return lp + lnL_Poisson(theta,yerr)
但是,当运行此代码时,它返回
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,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
代码在使用高斯对数似然法时确实有效。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?
解决方法
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