Likelihood Objective

Likelihood objectives are crucial in statistical modeling, aiming to maximize the probability of observed data given a model's parameters. Current research focuses on addressing challenges like zero-inflation (where true data values are incorrectly recorded as zero), improving the efficiency of likelihood calculations in complex models such as spatio-temporal point processes (often using score matching or pseudolikelihood approaches), and handling dependencies between data points in settings like time series analysis or genomic data. These advancements enhance the accuracy and reliability of statistical inference and prediction across diverse fields, impacting areas from healthcare (e.g., infection modeling) to finance (e.g., forecasting) and beyond.