我一直在尝试使用pymc3来构建MCMC过程。这样我就可以估计降雨数据集的分布参数。除了这个模块中已经包含的分布之外，我试图构建一些其他的分布可能性来适应这些数据。例如，一个三参数正态分布。实际上，我运行了模型，但不知何故，这种"MissingInputError"无论如何都会在采样过程之后发生。此外，我真的是pymc3的初学者，希望这不是一个困扰你们的愚蠢问题。非常感谢！
with pm.Model() as model:
a = pm.Normal('a',0,1)
b = pm.Normal("b",0,1)
c = pm.HalfCauchy('c' , 2)
def logp(x, a=0, b=0 , c =1 ):
return -0.5*np.log(2*np.pi) - np.log(c) - np.log(x-a) - (np.log(x-a)-b)**2/(2*c**2)
obs = pm.DensityDist("obs" , logp , observed=dict(x = X , a = a , b = b , c=c))
trace = pm.sample(draws = 10 , chains = 1)
以下是总回溯：
<ipython-input-9-8200b9d8270c>:12: FutureWarning: In v4.0, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.
trace = pm.sample(draws = 10 , chains = 1)
Only 10 samples in chain.
WARNING:pymc3:Only 10 samples in chain.
Auto-assigning NUTS sampler...
INFO:pymc3:Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
INFO:pymc3:Initializing NUTS using jitter+adapt_diag...
Sequential sampling (1 chains in 1 job)
INFO:pymc3:Sequential sampling (1 chains in 1 job)
NUTS: [c, b, a]
INFO:pymc3:NUTS: [c, b, a]
Sampling 1 chain for 1_000 tune and 10 draw iterations (1_000 + 10 draws total) took 3060 seconds.
INFO:pymc3:Sampling 1 chain for 1_000 tune and 10 draw iterations (1_000 + 10 draws total) took 3060 seconds.
---------------------------------------------------------------------------
MissingInputError Traceback (most recent call last)
<ipython-input-9-8200b9d8270c> in <module>
10 obs = pm.DensityDist("obs" , logp , observed=dict(x = X , a = a , b = b , c=c))
11
---> 12 trace = pm.sample(draws = 10 , chains = 1)
13
14
C:A_contentsTOOLSanaconda3libsite-packagespymc3sampling.py in sample(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, pickle_backend, **kwargs)
637 if idata_kwargs:
638 ikwargs.update(idata_kwargs)
--> 639 idata = arviz.from_pymc3(trace, **ikwargs)
640
641 if compute_convergence_checks:
C:A_contentsTOOLSanaconda3libsite-packagesarvizdataio_pymc3.py in from_pymc3(trace, prior, posterior_predictive, log_likelihood, coords, dims, model, save_warmup, density_dist_obs)
561 InferenceData
562 """
--> 563 return PyMC3Converter(
564 trace=trace,
565 prior=prior,
C:A_contentsTOOLSanaconda3libsite-packagesarvizdataio_pymc3.py in __init__(self, trace, prior, posterior_predictive, log_likelihood, predictions, coords, dims, model, save_warmup, density_dist_obs)
169
170 self.density_dist_obs = density_dist_obs
--> 171 self.observations, self.multi_observations = self.find_observations()
172
173 def find_observations(self) -> Tuple[Optional[Dict[str, Var]], Optional[Dict[str, Var]]]:
C:A_contentsTOOLSanaconda3libsite-packagesarvizdataio_pymc3.py in find_observations(self)
182 elif hasattr(obs, "data") and self.density_dist_obs:
183 for key, val in obs.data.items():
--> 184 multi_observations[key] = val.eval() if hasattr(val, "eval") else val
185 return observations, multi_observations
186
C:A_contentsTOOLSanaconda3libsite-packagestheanographbasic.py in eval(self, inputs_to_values)
552 inputs = tuple(sorted(inputs_to_values.keys(), key=id))
553 if inputs not in self._fn_cache:
--> 554 self._fn_cache[inputs] = theano.function(inputs, self)
555 args = [inputs_to_values[param] for param in inputs]
556
C:A_contentsTOOLSanaconda3libsite-packagestheanocompilefunction__init__.py in function(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)
335 # note: pfunc will also call orig_function -- orig_function is
336 # a choke point that all compilation must pass through
--> 337 fn = pfunc(
338 params=inputs,
339 outputs=outputs,
C:A_contentsTOOLSanaconda3libsite-packagestheanocompilefunctionpfunc.py in pfunc(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys)
522 inputs.append(si)
523
--> 524 return orig_function(
525 inputs,
526 cloned_outputs,
C:A_contentsTOOLSanaconda3libsite-packagestheanocompilefunctiontypes.py in orig_function(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys)
1968 try:
1969 Maker = getattr(mode, "function_maker", FunctionMaker)
-> 1970 m = Maker(
1971 inputs,
1972 outputs,
C:A_contentsTOOLSanaconda3libsite-packagestheanocompilefunctiontypes.py in __init__(self, inputs, outputs, mode, accept_inplace, function_builder, profile, on_unused_input, fgraph, output_keys, name)
1582 # make the fgraph (copies the graph, creates NEW INPUT AND
1583 # OUTPUT VARIABLES)
-> 1584 fgraph, additional_outputs = std_fgraph(inputs, outputs, accept_inplace)
1585 fgraph.profile = profile
1586 else:
C:A_contentsTOOLSanaconda3libsite-packagestheanocompilefunctiontypes.py in std_fgraph(input_specs, output_specs, accept_inplace)
186 orig_outputs = [spec.variable for spec in output_specs] + updates
187
--> 188 fgraph = FunctionGraph(orig_inputs, orig_outputs, update_mapping=update_mapping)
189
190 for node in fgraph.apply_nodes:
C:A_contentsTOOLSanaconda3libsite-packagestheanographfg.py in __init__(self, inputs, outputs, features, clone, update_mapping)
160
161 for output in outputs:
--> 162 self.import_var(output, reason="init")
163 for i, output in enumerate(outputs):
164 self.clients[output].append(("output", i))
C:A_contentsTOOLSanaconda3libsite-packagestheanographfg.py in import_var(self, var, reason)
340 "Computation graph contains a NaN. " + var.type.why_null
341 )
--> 342 raise MissingInputError("Undeclared input", variable=var)
343 self.setup_var(var)
344 self.variables.add(var)
MissingInputError: Undeclared input
- PyMC3版本：3.11.2
- Aesara/Theano版本：1.1.2
- Python版本：python3.8.8
- 操作系统：Windows 10
- 你是如何安装PyMC3的：(conda/pip(pip
您可能会得到错误，因为a、b、c不是观测值，但在第10行定义obs变量时，您仍将它们传递到observed关键字参数中。将代码中的第8-10行替换为以下内容：
def logp(x):
return -0.5*np.log(2*np.pi) - np.log(c) - np.log(x-a) - (np.log(x-a)-b)**2/(2*c**2)
obs = pm.DensityDist("obs" , logp , observed=X)