The Categorical Distribution, also known as the Generalized Bernoulli Distribution, is a discrete probability distribution that describes the probability of observing ...

CategoricalDistribution models a categorical probability distribution. In another words it learns how probable a thing is in a set of things. For example imagine a jar filled with unknown number of ...

Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to ...

The estimation of categorical distributions under marginal constraints summarizing some sample from a population in the most-generalizable way is key for many machine-learning and data-driven ...

We propose Gumbel Noise Score Matching (GNSM), a novel unsupervised method to detect anomalies in categorical data. GNSM accomplishes this by estimating the scores, i.e., the gradients of log ...

Many traits including shapes and colors of flowers, fruits and seeds in plants, as well as coat colors and some behavioral properties in animals, are recorded in discrete categories. If categories are ...

What are the Mixture Models? In the field of unsupervised learning, probabilistic models which represent the probability of the presence of clusters within the overall population can be considered as ...