Proceedings of the National Academy of Sciences of the United States of America, Vol. 108, No. 37 (September 13, 2011), pp. 15112-15117 (6 pages) Approximate Bayesian computation (ABC) have become an ...

Approximate Bayesian Computation (ABC) is a likelihood‐free inference methodology that has revolutionised the way researchers tackle complex problems where the likelihood function is difficult or ...

Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

The paper presents a Bayesian framework for the calibration of financial models using neural stochastic differential equations (neural SDEs), for which we also formulate a global universal ...

The Virtual Brain Inference (VBI) toolkit enables efficient, accurate, and scalable Bayesian inference over whole-brain network models, improving parameter estimation, uncertainty quantification, and ...

Concepts and algorithms of machine learning including version-spaces, decision trees, instance-based learning, networks, evolutionary computation, Bayesian learning and reinforcement learning.

Phase 1a/1b study in 97 patients demonstrates mitochondrial metabolic reprogramming induced by BPM31510-IV and supports advancement in multiple ...