35 lines
742 B
Python
35 lines
742 B
Python
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import numpy as np
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import pymc as pm
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import arviz as az
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from matplotlib import pyplot as plt
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x = np.random.normal(3, 2, size=30)
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# plt.hist(x)
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# plt.show()
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with pm.Model() as model:
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mu = pm.Uniform('mu', lower=-5, upper=5)
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sig = pm.HalfNormal('sig', sigma=10)
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data = pm.Normal('data', mu=mu, sigma=sig, observed=x)
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pm.model_to_graphviz(model)
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with model:
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trace = pm.sample(5000, tune=1000)
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# plt.plot(trace.posterior.data_vars["mu"][0, 1:100])
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# az.plot_trace(trace)
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summary = az.summary(trace)
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with model:
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post = pm.sample_posterior_predictive(trace)
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plt.hist(post.posterior_predictive.data.to_numpy().flatten(), density=True, bins=50)
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plt.hist(x, alpha=0.4, density=True, bins=6)
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az.plot_ppc(post)
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plt.show()
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