Lectures related to Markov Chains: Definition and Examples

Covers countable and uncountable sets, sequences, summation, and Cantor's Diagonalization proof.

Covers stochastic processes, focusing on Markov chains and their applications in real-world scenarios.

Covers cardinality of sets, poset, equivalence relations, and geometric progressions through a quiz on Kahoot.

Introduces Markov chains and Metropolis Hastings algorithm in stochastic simulation.

Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.

Introduces Cartesian product and induction for proofs using integers and sets.

Explores path integrals in quantum field theory, emphasizing the significance of Wick rotation and the classical case.