Markov Chains: Basics and Applications

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 2 of 4

Discrete-Time Markov Chains: Definitions

Covers the definitions and state probabilities of discrete-time Markov chains.

Hidden Markov Models: Primer

Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.

Applied Probability & Stochastic Processes

Covers applied probability, Markov chains, and stochastic processes, including transition matrices, eigenvalues, and communication classes.

Stochastic Models for Communications

Covers mathematical tools for communication systems and data science, including information theory and signal processing.

Stochastic Models for Communications

Covers the fundamentals of stochastic models for communications, focusing on Markov chains and Poisson processes.

Modeling Neurobiological Signals: Markov Chains

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

Modeling Neurobiological Signals: Spikes & Firing Rate

Explores modeling neurobiological signals, focusing on spikes, firing rate, multiple state neurons, and parameter estimation.

Stochastic Processes: Markov Chains

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

Stochastic Processes: Generation and Embedding

Explores the generation of stochastic processes, including Gaussian processes, Markov processes, Poisson processes, and circulant embedding.

Simulation & Optimization: Poisson Process & Random Numbers

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.