Probability Mass

Probability mass, representing the likelihood of specific outcomes in a probability distribution, is a fundamental concept with applications across diverse fields. Current research focuses on accurately estimating missing or underrepresented probability mass, particularly in complex scenarios like Markovian sequences and high-dimensional data, employing techniques such as Good-Turing estimators and neural score matching within Bayesian frameworks. These advancements improve the reliability of probabilistic models in various applications, ranging from natural language processing and generative model assessment to cosmological mass mapping, where accurate probability mass estimation is crucial for robust inference and uncertainty quantification. Improved estimation methods lead to more accurate predictions and a deeper understanding of underlying processes in these diverse fields.