Many application domains, such as ecology or genomics, have to deal with multivariate non-Gaussian observations. A typical example is the joint observation of the respective abundances of a set of ...

Yoshihiro Tawada proposes using variational inference – a technique widely used in machine learning – to obtain foreign exchange implied volatilities with nonlinear constraints for strike-order ...

Probabilistic programming has emerged as a powerful paradigm that integrates uncertainty directly into computational models. By embedding probabilistic constructs into conventional programming ...

Latent space models are popular for analyzing dynamic network data. We propose a variational approach to estimate the model parameters and the latent positions of the nodes in the network. The ...

SHENZHEN, China, May 2, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO) announced today the launch of their latest classifier auto-optimization technology based on ...

The Department of Computer Science, Faculty of Science, University of Helsinki invites applications for a Postdoctoral Researcher in Probabilistic Machine Learning and Amortized Inference. The ...