Category "bayesian"

Normal-gamma distribution in Python

Is there an implementation of the Normal-Gamma distribution for Python? I have looked over the internet, including scipy, and could not find it.

Negative BIC values for GaussianMixture in scikit-learn (sklearn)

In scikit-learn, the GaussianMixture object has the method bic(X) that implements the Bayesian Information Criterion to choose the number of components that bet

RJ AGS: hierarchical model expected utility: unable to estimate subject level parameters properly

I am trying to fit simulated data to a hierarchical expected utility model, where the priors of the individual simulated subjects are informed by group level pr

Trying to replicate figures from Bayesian statistics without tears: A sampling-resampling perspective, but failed

I'm trying to replicate the three figures from the paper Bayesian statistics without tears: A sampling-resampling perspective, which can be fo

Problem in running PYMC3 - I cannot do sampling and get the trace results

I am a new learner in PYMC3 and I installed the package recently and i tried to run a sample example from the pcmc3 avilbale examples which is this one: Binomia

Posterior distribution for randomized responses

Suppose you want to know how many people cheat on their taxes. If you ask them directly, it is likely that some of the cheaters will lie. You can get a more acc

How to Simulate a Biased 6-sided Dice using Pymc3?

How do I simulate a 6-side Dice roll using Pymc3? Also, what is I know that different sides of the dice have different distributions?

Two methods of recovering fitted values from a Bayesian Structural Time Series model yield different results

Two conceptually plausible methods of retrieving in-sample predictions (or "conditional expectations") of y[t] given y[t-1] from a bsts model yield different re

MCMCglmm gives very different results from lme4 - how to diagnose issue?

I am taking the plunge into Bayesian analysis for some new projects. I have some yes/no data, and three fixed effects and for the time being, I'm simply taking

How to use `Dirichlet Process Gaussian Mixture Model` in Scikit-learn? (n_components?)

My understanding of "an infinite mixture model with the Dirichlet Process as a prior distribution on the number of clusters" is that the number of clusters is d