Course

MGT-484: Applied probability & stochastic processes

Lectures in this course (23)

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.

Covers communicating classes, hitting time, probabilities, and the Gambler's Ruin Model in stochastic processes.

Explores birth and death chains, hitting probabilities, and expected game durations in Markov chains.

Explores recurrence and transience in Markov chains, discussing the strong Markov property and state classifications.

Explores invariant distributions, recurrent states, and convergence in Markov chains, including practical applications like PageRank in Google.

Explores Markov chain convergence, emphasizing invariant distribution, Law of Large Numbers, and mean rewards computation.

Covers Markov chains decomposition, LLN proof, Inventory Model application, and average costs.

Covers the (s,s)-inventory model, stationary Markov chains, time reversal property, and detailed balance condition.

Explores controlled stochastic processes, focusing on analysis, behavior, and optimization, using dynamic programming to solve real-world problems.

Explores optimal hunting strategies, uncertain oil prices, and linear cost-minimization policies.

Explores Dynamic Programming for optimal control, focusing on stability, stationary policy, and recursive solutions.

Covers a recap of dynamic programming and optimal betting strategy in a gambling problem.

Explores dynamic portfolio selection, log-utility functions, risk aversion, and optimal control problems in financial markets.

Discusses asset selling problems and optimal selling policies based on market conditions and termination states.

Covers optimal stopping problems in applied probability and stochastic processes, focusing on theory and practical applications.

Explores dynamic programming in finance, emphasizing optimal prediction strategies in market scenarios.

Explores the marriage problem, where a bride must strategically accept suitors to maximize her chances of selecting the best one.

Covers infinite-horizon problems in Applied Probability and Stochastic Processes.

Explores the Asset Selling Problem to maximize long-term reward without a deadline.

Explores the motivation and algorithm behind the Monte Carlo Chain method.