Continuous-Time Markov Chains: Definitions and State Probabilities

Related lectures (31)

Page 3 of 4

Covers the criterion for recurrence in infinite chains based on the expected number of visits in a state.

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

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.

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

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.

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

Introduces the concept of a Markov process and chain in configuration sampling.

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