Explores equilibrium in Markov Chains, covering invariant distributions, properties determination, and practical applications.
Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.
Explores Markov chain convergence, emphasizing invariant distribution, Law of Large Numbers, and mean rewards computation.
Explores the concept of stationary distribution in Markov chains, discussing its properties and implications, as well as the conditions for positive-recurrence.
