Continuous-Time Markov Chains: Definitions and State Probabilities

Related lectures (31)

Page 3 of 4

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Explores NISQ devices and IBM Q, covering noisy quantum circuits, qubit technologies, and quantum algorithm development.

Covers random walks on a lattice, focusing on return probabilities and their dependence on dimensionality.

Covers free fields and probabilistic functions, focusing on random variables and their properties.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Introduces the key concepts of convexity and its applications in different fields.

Explores probability inequalities, convergence types, and moment generating functions for distribution approximation.

Explores Markov chains and stationary distributions, emphasizing the importance of identifying them for improving convergence.

Explores challenges and surprises in Markov chains, analyzing convergence, state grouping, and transition probabilities.

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.