Markovian Switching

Markovian switching models analyze systems that transition between different states or regimes governed by a Markov process, aiming to capture abrupt changes in dynamics often missed by traditional constant-coefficient models. Current research focuses on parameter inference within these models, particularly for complex systems like partial differential equations and high-dimensional time series, employing techniques such as Bayesian methods and Mixture Density Networks. These advancements are improving the accuracy and applicability of Markovian switching in diverse fields, from modeling stochastic electronic devices and climate data to enabling more robust machine learning algorithms.