Category : pymc3

I am trying to plot the multi-switchpoint posterior distributions in python, pymc3 and matplotlib, but the graph output looks different from the posterior distributions, can anyone tell me what I am doing wrong, please? And my code for the plot is below early_rate_samples = trace[‘lambda_1’] late_rate_samples = trace[‘lambda_2’] later_rate_samples = trace[‘lambda_3’] changepoint_samples = trace[‘tau_1’] changepoint_2_samples ..

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I am trying to convert PyMC code to PyMC. I didn’t find a comprehensive guide on differences and I get an error just changing pymc to pm. I would appreciate the help! #hyperpriors home = pymc.Normal(‘home’, 0, .0001, value=0) tau_att = pymc.Gamma(‘tau_att’, .1, .1, value=10) tau_def = pymc.Gamma(‘tau_def’, .1, .1, value=10) intercept = pymc.Normal(‘intercept’, 0, ..

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I get this error: Exception: (‘Compilation failed (return status=1): cc1plus.exe: sorry, unimplemented: 64-bit mode not compiled in. ‘, ‘FunctionGraph(Elemwise{add,no_inplace}(TensorConstant{1.0}, TensorConstant{1.0}))’) when I try to run this model: np.random.seed(123) n_experiments = 4 theta_real = 0.35 data = stats.bernoulli.rvs(p=theta_real, size= n_experiments) firstModel= pm.Model() with firstModel: theta=pm.Beta( ‘theta’, alpha=1, beta=1) y= pm.Bernoulli(‘y’, p=theta, observed= data) trace=pm.sample(1000, cores= 4) ..

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I would like to identify divergences in a chain sampled by pymc3. I am using Arviz.InferenceData to plot the trace of the chain (where each line represents one group): import arviz as az azdata = az.from_pymc3( trace=trace, coords={‘group’: groups, ‘condition’: conditions}, dims={‘a_kg’: [‘group’, ‘condition’]} ) azdata_sel = azdata.sel(chain=[0], condition=’Control’) az.plot_trace(azdata_sel, var_names=[‘a_kg’], divergences=’bottom’); Only one chain ..

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I’m adapting my code using hierarchical model to the notebook in pymc3 (https://docs.pymc.io/notebooks/updating_priors.html). And I’m struggling with plotting the traces since one of my parameters (lam) is an array. Below is my code for the pymc3 model: model1 = pm.Model() with model1: # Priors are posteriors from previous iteration mu = from_posterior("mu", trace["mu"]) sigma = ..

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Disclaimer: SO is forcing me to express stdout output copied below as code. Simple quoting wouldn’t allow me to post I have a pyproject.toml file from a Proof of Concept that worked beautifully a few months ago, which includes: [tool.poetry.dependencies] theano = "^1.0.5" pymc3 = "^3.9.3" I copied this over to start extending from the ..

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