Stochastic Processes: Spectral Density, Markov Chains, and Gaussian Variables

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 3 of 4

Explores mean, variance, probability functions, inequalities, and various types of random variables, including Binomial, Geometric, Poisson, and Gaussian distributions.

Probability Distributions in Environmental Studies

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Continuous Random Variables: Basics

Explores continuous random variables, cdf, pdf, convolution, and sum of dice.

Binomial Distribution in R

Introduces the Binomial distribution in R with examples of n coin tosses.

Causal Systems & Transforms: Delay Operator Interpretation

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Binary Covariate Impact: 2x2 Contingency Tables

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Gaussian Vectors: Properties and Distributions

Explains multivariate Gaussian distribution properties and moment generating functions for random vectors.

Modeling Neurobiological Signals: Markov Chains

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

The cross-nested logit model

Explores the cross-nested logit model, defining alternative groups with overlapping memberships and scale parameters for transportation analysis.

Markov Chain Monte Carlo: Rejection Sampling

Explores rejection sampling for generating sample values from a target distribution, along with Bayesian inference using MCMC.