Dynkin's theorem is a fundamental result in probability theory that connects the concepts of Markov processes and stopping times. It states that if a process is a Markov process, then the expected value of a function of the process at a stopping time can be expressed in terms of the initial state of the process. This theorem is crucial for understanding how information evolves over time in stochastic processes. The theorem is named after Eugene Dynkin, who contributed significantly to the field of probability. It provides a powerful tool for analyzing various problems in stochastic calculus and has applications in areas such as finance, insurance, and statistical mechanics.