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 ...

For a long time, filtered backprojection (FBP) has been the only reconstruction algorithm used in SPECT. However, it appears that the more widely available and increasingly fast iterative ...

Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.

In recent years, a learning method for classifiers using tensor networks (TNs) has attracted attention. When constructing a classification function for high-dimensional data using a basis function ...

Abstract: The convergence of expectation-maximization (EM)-based algorithms typically requires continuity of the likelihood function with respect to all the unknown parameters (optimization variables) ...

Abstract: Message passing algorithms have had dramatic impacts on important problems in signal processing, learning theory, communication theory, and information theory through their computational ...

Sequentia is a Python package that provides various classification and regression algorithms for sequential data, including methods based on hidden Markov models and dynamic time warping. Some ...

One fine summer morning, you show up for a scheduled appointment for your regular checkup at the highly rated local health clinic. You walk in feeling fine. You raise your arm and say, “Doc, it hurts ...

Filtered backprojection (FBP) algorithms reduce image noise by smoothing the image. Iterative algorithms reduce image noise by noise weighting and regularization. It is believed that iterative ...