Transition Probability

Transition probability, the likelihood of moving between states in a system, is a fundamental concept across diverse scientific fields, with research focusing on accurately estimating and utilizing these probabilities for prediction and control. Current research employs various models, including Markov chains, hidden Markov models, and reinforcement learning algorithms, often coupled with machine learning techniques to improve estimation accuracy, particularly in complex or high-dimensional systems with noisy or incomplete data. Accurate transition probability estimation is crucial for applications ranging from enrollment prediction in universities to optimizing control strategies in robotics and improving the understanding of complex phenomena like climate change and disease spread.