bayesian

I need a library for naïve Bayes large scale, with millions of training examples and +100k binary features. It must be an online version (updatable after training). I also need top-k output, that is multiple classifications for a single instance. Accuracy is not very important. The purpose is an automatic text categorization application. Any suggestions for a good library is very appreciated. EDIT: The library should preferably be in Java. If a learning algorithm other than naïve Bayes is also acceptable, then check out Vowpal Wabbit (C++), which has the reputation of being one of the best

I'm trying to convert this example of Bayesian correlation for PyMC2 to PyMC3, but get completely different results. Most importantly, the mean of the multivariate Normal distribution quickly goes to zero, whereas it should be around 400 (as it is for PyMC2). Consequently, the estimated correlation quickly goes towards 1, which is wrong as well. The full code is available in this notebook for PyMC2 and in this notebook for PyMC3 . The relevant code for PyMC2 is def analyze(data): # priors might be adapted here to be less flat mu = pymc.Normal('mu', 0, 0.000001, size=2) sigma = pymc.Uniform(

I run a calendar photo contest that uses a 5-star rating system which ranks the images according to their average rating. However, I would like to factor in the total number of votes a photo receives to get a more accurate ranking. For example, I do not want an image with 1 5-star vote (Avg rating: 5) getting ranked above an image with 10 5-star votes and 1 4-star vote (Avg rating: 4.9). I know this topic has been raised before, but I can't seem to find a straightforward answer to apply to my particular situation. The Evan Miller site goes way over my head… I'm just looking for a simple

I would like to use a Bayesian multivariate linear regression to estimate the strength of players in team sports (e.g. ice hockey, basketball or soccer). For that purpose, I create a matrix, X, containing the players as columns and the matches as rows. For each match the player entry is either 1 (player plays in the home team), -1 (player plays in the away team) or 0 (player does not take part in this game). The dependent variable Y is defined as the scoring differences for both teams in each match (Score_home_team - Score_away_team). Thus, the number of parameters will be quite large for one

This question was migrated from Cross Validated because it can be answered on Stack Overflow. Migrated 4 years ago . I've implemented the Bayesian Probabilistic Matrix Factorization algorithm using pymc3 in Python. I also implemented it's precursor, Probabilistic Matrix Factorization (PMF). See my previous question for a reference to the data used here. I'm having trouble drawing MCMC samples using the NUTS sampler. I initialize the model parameters using the MAP from PMF, and the hyperparameters using Gaussian random draws sprinkled around 0. However, I get a PositiveDefiniteError when

I'm relatively new to PYMC3 and I'm trying to implement a Bayesian Structure Time Series (BSTS) without regressors, for instance the model fit here in R. The model is as follows: I can implement the local linear trend using a GaussianRandomWalk as follows: delta = pymc3.GaussianRandomWalk('delta',mu=0,sd=1,shape=99) mu = pymc3.GaussianRandomWalk('mu',mu=delta,sd=1,shape=100) However, I'm at a loss for how to encode the seasonal variable (tau) in PYMC3. Do I need to roll a custom random walk class or is there some other trick? You can use w = pm.Normal('w', sd=sigma_tau, shape=S) tau = w - tt