Category : pymc3

I have been working using the following codes to acquire the Bayesian Fusion of StudentT distribution. Fusion code: def S2_0_Bayesian_Interface(data): ######################################################################################################################## with pm.Model() as model_0: # Prior Distributions for unknown model parameters: nu_0 = pm.HalfNormal(‘nu_0’, sd=1) sigma_0 = pm.HalfNormal(‘sigma_0’, sd=5) mu_0 = pm.Normal(‘mu_0’, mu=0, sd=5) # Observed data is from a Likelihood distributions (Likelihood (sampling ..

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I have some issues with theano package on my mac. I am getting the following error. File "/Users/model.py", line 355, in train train_model = theano.function( File "/Applications/anaconda3/lib/python3.8/site-packages/theano/compile/function/__init__.py", line 337, in function fn = pfunc( File "/Applications/anaconda3/lib/python3.8/site-packages/theano/compile/function/pfunc.py", line 426, in pfunc inputs = [ File "/Applications/anaconda3/lib/python3.8/site-packages/theano/compile/function/pfunc.py", line 427, in <listcomp> _pfunc_param_to_in(p, allow_downcast=allow_input_downcast) for p in params ..

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I am a complete beginner in probabilistic programming so i am having some problems. For the purpose of my dissertation project, I need to evaluate the efficacy of a certain vaccine against Covid-19 using Bayesian methods. As it turns out, my population parameter (infection process, i.e., number of people infected) follows a Poisson prior, and ..

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I ran into this piece of code: data = np.repeat((0, 1), (3, 6)) with pm.Model() as normal_approximation: p = pm.Uniform(‘p’, 0, 1) w = pm.Binomial(‘w’, n=len(data), p=p, observed=data.sum()) mean_q = pm.find_MAP() # MAP: maximum a posteriori probability std_q = ((1/pm.find_hessian(mean_q, vars=[p]))**0.5)[0] mean_q[‘p’], std_q I tried to search google generally and look at the documentation at ..

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