I'm trying to perform a multinomial logistic regression in R employing the Metropolis-Hastings algorithm, considering a Matrix Normal Distribution as proposal. I'm using the function rmatrixnorm() included in the package LaplacesDemon in order to sample from the proposal distribution. I followed this strategy since I need a vector of parameter $\underline{\beta_{k}}$, with $k=1,\dots,K$ (number of classes involved in the classification). At the end of the Monte-Carlo iterations, my procedure retrieves the sample mean and the sample covariance of the posterior …

I have the following model im pymc3. import pandas as pd import pymc3 as pm import numpy as np import arviz as az with basic_model: lambda1 = pm.Gamma("lambda1", alpha=0.001, beta=0.001) p = pm.Beta('p', 1, 1,) z = [0.0] * len(x['Length'].values) Y_obs = [0.0] * len(x['Length'].values) for i in range(len(x['Length'].values)): z[i] = pm.Bernoulli('z[i]',p) Y_obs[i] = pm.Poisson("Y_obs[i]", mu=lambda1*z[i]*x['Length'].values+0.001, observed=x['Count'].values[i]) trace = pm.sample(7000, tune=2000, cores=1, return_inferencedata=True) x is a data frame that is read form a csv file. It is producing the error …

In pymc3 documentation, it specifies that the .dist() member for distribution allows the distribution to be used without a model for sampling and use of the logp functions e.g. d_dist = pm.HalfCauchy.dist(beta=2.5, shape=3) however, an example I copied, used .dist() functions for model elements, but when I remove the .dist() and use the model version of the distribution, it fails. so: data = [tuple(x) for x in self.pointdata[['x','y', 'z']].values] print("Data {}".format(data)) with pm.Model() as model: mu1 = pm.Normal('mu_wall1' ,mu=0.0, sd=10.0, …

There are three variables, X3 is a function of X1 and X2, X2 also depends on X1.The dependency relationship is shown as the following graph. In specific, X3= f(X1, x2) and X2=g(X1). Therefore, X3=f(X1, g(X1)). If the probabilistic distribution of X1 is known, how to get the probabilistic distribution of X3.

I'm trying to run a simple logistic regression on PyMC3. Here the code: with pm.Model() as logistic_model: alpha=pm.Normal('alpha', mu=0, sd=100) beta1=pm.Normal('beta', mu=0, sd=100) beta2=pm.Normal('beta2', mu=0, sd=100) argtemp=alpha + beta1*data['age'].values +beta2*data['educ'].values prob=pm.invlogit(argtemp) Y_obs = pm.Bernoulli('Y_obs', p=prob, observed=data['income_more_50K'].values) map_estimate = pm.find_MAP() sampler=pm.Metropolis() trace= pm.sample(40000, step=sampler, start=map_estimate) The sampler run, but at the end it gives this error: The gelman-rubin statistic is larger than 1.4 for some parameters. The sampler did not converge. The estimated number of effective samples is smaller than 200 …

I am training an MCMC model in using Pymc3. My aim is to build a series of linear regression models which will predict the time to unload a truck, based on the number of crates to unload. I have data for 2000 locations and they are divided into 4 categories of location. Locations of the same category tend to have similar unloading times. For each location I have a series of recorded data points: number of crates on the truck …