Lectures related to Stochastic Models for Communications

Stochastic Models for Communications

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Markov Chains: State Classification

Covers the classification of states in Markov chains.

Continuous Time Markov Chains

Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.

Generation of Markov Processes

Covers the generation of Markov processes and Markov chains, including transition matrices and stochastic matrices.

Markov Chains: Basics and Applications

Introduces Markov chains, covering basics, generation algorithms, and applications in random walks and Poisson processes.

Applied Probability & Stochastic Processes

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

Markov Chains: State Classification

Covers the classification of states in discrete-time Markov chains.

Continuous-Time Markov Chains: Kolmogorov Equations

Covers continuous-time Markov chains and Kolmogorov equations in stochastic communication models.

Continuous-Time Markov Chains: Kolmogorov Equations

Covers the equations of Kolmogorov for continuous-time Markov chains.

Stochastic Simulation: Markov Processes Generation

Covers the generation of Markov processes and Poisson processes in stochastic simulation.

Invariant Distributions: Markov Chains

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

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Markov Chains: Transition Densities

Covers Markov processes, transition densities, and distribution conditional on information, discussing classification of states and stationary distributions.

Stochastic Models for Communications

Covers stochastic models for communication systems, including concepts like stochastic processes and Markov chains.

Neurobiological Signals: Processing and Classification

Explores neurobiological signal processing, including spike modeling, de-noising, and data classification techniques.

Neurobiological Signal Processing

Explores neurobiological signal processing, covering spike modeling, signal classification, and data characterization using principal component analysis.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Discrete-Time Markov Chains: Definitions

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

Markov Chains: Properties and Expectations

Explores Markov chains' properties, expectations, and recurrence in Poisson processes.

Stochastic Models for Communications

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