Exercise 2: Series 22

Related lectures (30)

Page 3 of 3

Covers random variables, probability density functions, Gaussian distribution, and correlation in statistics.

Explores combinatorial mathematics, covering permutations, combinations, and binomial coefficients, along with probability and statistics concepts.

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

Covers the fundamentals of probability theory, including corollaries, conditional probability, total probability theorem, and random variables.

Introduces Fisher Information for parameter estimation based on collected data.

Covers phase space, ensembles of particles, probability distributions, and probability density functions.

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

Covers the fundamentals of probability theory, including conditional probability, Bayes' rule, and random variables.

Introduces the concept of the distribution of a random variable and explores specific cases of discrete and continuous random variables.

Covers the fundamentals of probability theory, including notations for joint and conditional distributions.