Speaker
Description
ParaMonte (short for Parallel Monte Carlo: https://github.com/cdslaborg/paramonte) is a library of Monte Carlo and Machine Learning routines, available in both serial and parallel versions, for general common scientific inference, particularly tasks involving data analysis, optimization, sampling, and integration of mathematical density functions, and exploration of the posterior distributions in Bayesian models and inverse problems. The library has been designed with a strong focus on unifying automation, ease of use, high performance, scalability, and reproducibility. We will discuss the library's facilities for end-users and its evolution to a multi-precision, fully-generic interface library, comprising nearly 2000 functionalities for machine learning and scientific inference.
