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 ...

Abstract: This paper introduces a robust identification solution for the linear parameter varying Autoregressive Exogenous systems with outlier-contaminated outputs. The Laplace distribution with ...

Ramping the resolution The QST research team with the new high-resolution PET scanner. From left to right: Hidekatsu Wakizaka, Taiga Yamaya, Han Gyu Kang, Makoto Higuchi and Hideaki Tashima. (Courtesy ...

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 ...

Time-of-flight (TOF) PET uses very fast detectors to improve localization of events along coincidence lines-of-response. This information is then utilized to improve the tomographic reconstruction.

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) ...

Data clustering is the process of grouping data items so that similar items are placed in the same cluster. There are several different clustering techniques, and each technique has many variations.

Recent advances in computing have accelerated researchers’ ability to amass and analyze data. University of California, Los Angeles mathematical scientist Kenneth Lange uses his expertise in ...