Probability Space

A probability space mathematically formalizes uncertainty, defining a set of possible outcomes, their associated probabilities, and a measure to quantify these probabilities. Current research focuses on refining the axiomatic foundations of probability spaces, particularly in relation to causality and decision-making, exploring novel neural network architectures for learning functions of distributions within these spaces, and developing methods for efficiently representing high-dimensional probability spaces for improved computational efficiency. These advancements have implications for diverse fields, including machine learning, causal inference, and the solution of complex scientific problems involving uncertainty quantification.