I'm trying to sort a bunch of products by customer ratings using a 5 star system. The site I'm setting this up for does not have a lot of ratings and continue to add new products so it will usually...

Based on a given network structure, I created a data frame of 100 instances for six binary variables(x1 to x6). So it's a 100 x 6 data frame of 0/1 values stored in a variable 'input_params'. Creat...

I am struggling to implement a linear regression in pymc3 with a custom likelihood. I previously posted this question on CrossValidated & it was recommended to post here as the question is more...

With the dataframe underneath I want to optimize the total return, while certain bounds are satisfied. d = {'Win':[0,0,1, 0, 0, 1, 0],'Men':[0,1,0, 1, 1, 0, 0], 'Women':[1,0,1, 0, 0, 1,1],'Matches'...

I am trying to find/figure out a function that can update probabilities. Suppose there are three players and each of them get a fruit out of a basket: ["apple", "orange", "...

I am able to successfully improve the performance of my XGBoost model through Bayesian optimization, but the best I can achieve through Bayesian optimization when using Light GBM (my preferred choi...

Background: Normally, R gives quantiles for well-known distributions. Out of these quantiles, the lower 2.5% up to the upper 97.5% covers 95% of the area under these distributions. Question: Sup...

I'm trying to understand how to use a black box likelihood function in pymc. Basically, this is explained here. I have tried implementing this on my own with a very simple Python model (a double lo...

I need to provide a weighted sort on 2+ factors, ordered by "relevancy". However, the factors aren't completely isolated, in that I want one or more of the factors to affect the "urgency" (weight) ...

I am trying to fit a multinomial logistic regression model using rjags. The outcome is a categorical (nominal) variable (Outcome) with 3 levels, and the explanatory variables are Age (continuous) a...

I am trying to implement Bayesian CNN using Mc Dropout on Pytorch, the main idea is that by applying dropout at test time and running over many forward passes , you get predictions from a variety o...

I'm starting to learn Stan. Could anyone explain when and how to use syntax such as... ? target += instead of just: y ~ normal(mu, sigma) For example in Stan manual you can find the followi...

Suppose I have a following formula for a mixed effects model: Performance ~ 1 + WorkingHours + Tenure + (1 + WorkingHours + Tenure || JobClass) then I can specify priors for fixed slopes and fix...

What is the difference between a Bayesian network and a Naive Bayes classifier? I noticed one is just implemented in Matlab as classify the other has an entire net toolbox. If you could explain i...

I am trying to wrap my head around the concept of probabilistic programming but the more I read, the more I feel confused. My understanding at this point in time is that probabilistic programming ...

I am building a binomial regression model using 2 categorical variables. This is from an example in the book, Statistical rethinking. In the book, while using the rethinking package, we can set pri...

As I know, DropoutWrapper is used as follows __init__( cell, input_keep_prob=1.0, output_keep_prob=1.0, state_keep_prob=1.0, variational_recurrent=False, input_size=None, dtype=None, seed=None ) ...

I'm attempting to create a three-level logistic regression model in pymc3. There is a top level, mid level, and an individual level, where the mid-level coefficients are estimated from top-level co...

I'm developing a Bayesian regression model through rstanarm that combines multinomial, binomial, and scale predictors on a scale dependent variable. As a regular model, my model would look as it do...

I am searching for a hyperparameter tune package for code written directly in Tensorflow (not Keras or Tflearn). Could you make some suggestion?

This is my attempt at applying BayesSearch in CatBoost: from catboost import CatBoostClassifier from skopt import BayesSearchCV from sklearn.model_selection import StratifiedKFold # Classifier b...

Literature says that the metropolis-hasting algorithm in MCMC is one of the most important algorithms developed last century and is revolutional. Literature also says that it is such development in...

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 determined by the data as they converg...

I want to incorporate weights into the likelihood of a WINBUGS model to do what brms does with weights. The usual BUGS approaches to accomplish that for dnorm and dpois are not working for dbin. ...

Is there a possibility to extract the stan code used for the MCMC sampling in rstanarm? I would like to compare my own parametrisation of a model and prior choices to the one used in rstanarm.