Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

The expectation-maximization (EM) algorithm is a cornerstone technique for parameter estimation in statistical models that incorporate latent variables or incomplete data. By iteratively alternating ...

Abstract: Over the past decades, there has been a surge of interest in studying low-dimensional structures within high-dimensional data. Statistical factor models (i.e., low-rank plus diagonal ...

Abstract: Bayes classifier employs a statistical model to categorize data. It is regarded as the most effective classifier when its probabilistic models are able to extract sufficient information from ...

Diffusion models have emerged as powerful generative priors for high-dimensional inverse problems, yet learning them when only corrupted or noisy observations are available remains challenging. In ...