Bayesian Estimation

Bayesian estimation is a statistical framework for updating beliefs about unknown parameters based on observed data, aiming to quantify uncertainty and improve prediction accuracy. Current research emphasizes developing efficient algorithms, such as particle filters, harmonic exponential filters, and Markov Chain Monte Carlo methods, to handle complex scenarios with high dimensionality, non-linearity, multimodal distributions, and censored data, often within distributed or privacy-preserving settings. These advancements are crucial for diverse applications, including robotics, predictive maintenance, active learning, and privacy-preserving machine learning, by enabling more robust and reliable inferences from noisy or incomplete data.