I would like to use the equivalent of 2 functions from the lib. pymc using the lib. pymc3 or another library compatible with Python 3.8. The 2 functions are: pm.stochastic and pm.MCMC. Do you have any suggestion ? Many thanks in advance Best regards Nicolas Source: Python..

#### Category : pymc3

pymc3.stats.summary function clearly documented at https://pymc3-testing.readthedocs.io/en/rtd-docs/api/stats.html Yet an attempt to use this function pymc3.stats.summary(self.trace_y_j_t, include_transformed = True, to_file = txt_filename) results in, e.g. TypeError: summary() got an unexpected keyword argument ‘include_transformed’ Removing the include_transformed arg and going with to_file only gives the same sort of error for that argument. PyMC3 is somewhat infamous for poor ..

import pymc3 as pm import numpy as np x = np.linspace(0,1,100) y_true = 3*x + 5 y_obs = y_true + np.random.normal(loc=0, scale=0.02,size=100) with pm.Model() as model: a = pm.Normal(‘a’, mu=2.0, sigma=3.0) b = pm.Normal(‘b’, mu=2.0, sigma=3.0) y_model = a*x + b s = pm.HalfNormal(‘s’, sigma=0.05) likelihood = pm.Normal(‘y’, mu=y_model, sigma=s, observed = y_obs) trace = ..

I have a fitting procedure written in pymc3 which utilizes dot products of matrices which are functions of random variables. On the original machine on which I wrote the program, it would run and by default use all available cores, as expected. However, I recently started using a brand new machine, including a clean installation ..

I am trying to extend the ideas of item response theory to multiple responses. Consider a marketing survey, which asks customers, "what’s the deciding factor in whether or not you purchase product X?" Where answers are {0: price, 1: durability, 2: ease-of-use}. Here is some synthetic data (rows are customers, columns are products, each cell ..

I’m seeking some wisdom on how to construct a hierarchical Bayesian model. The project is too complex to be useful to include here as-is. I could possibly craft a minimal toy example, but the simplicity of that would drive thinking in a direction inappropriate for the complex case. So I hope discussing it conceptually will ..

I’m learning the basics of PyMC3. In a spine regression section, 4.74, the author of this notebook, uses the following code: from patsy import dmatrix B = dmatrix( "bs(year, knots=knots, degree=3, include_intercept=True) – 1", {"year": d2.year.values, "knots": knot_list[1:-1]}, ) I’ve never used Patsy before, though I’ve consulted the documentation: https://patsy.readthedocs.io/en/latest/API-reference.html https://patsy.readthedocs.io/en/latest/formulas.html#formulas From what I’ve gained, ..

I have fit a two-component mixture model using PYMC3, and wish to sample from the posterior model. I am able to do so fine using pm.sample_posterior_predictive(), however when I attempt to do so using pm.fast_sample_posterior_predictive(), I receive the following ValueError: ValueError: input operand has more dimensions than allowed by the axis remapping I am unsure ..

I am trying to configure PyMC3 Polynomial kernel with the following hyperpriors; with pm.Model() as self.model: EPSILON = 0.1 l = pm.Gamma("l", alpha=2, beta=1) offset = pm.Gamma("offset", alpha=2, beta=1) nu = pm.HalfCauchy("nu", beta=1) d = pm.HalfNormal("d", sd=5) cov = nu ** 2 * pm.gp.cov.Polynomial(X.shape[1], l, d, offset) self.gp = pm.gp.Marginal(cov_func=cov) sigma = pm.HalfCauchy("sigma", beta=1) y_ ..

I am trying to build a simple model using PyMC3. The model should draw 10 samples from 10 discrete uniform distributions. Those 10 samples should then be used to calculate the difference between each consecutive sample, take the absolute value and calculate the mean. The mean of the differences should then be used as the ..

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