Insurance Risk Modeling

Related lectures (29)

Page 3 of 3

Covers advanced probability concepts, including random variables and expected value calculation.

Covers random variables, probability functions, expectations, variances, and joint distributions.

Covers fundamental concepts of probability theory and measure theory, including joint probabilities, random variables, and the central limit theorem.

Covers statistical modelling basics, probability concepts, and random variables for data analysis.

Covers the pdf of the sum of continuous random variables, convolution, and transformations like Y=aX.

Explores convergence in probability, concentration inequalities, laws of large numbers, and properties of distributions.

Covers random vectors, joint probability density, independent random variables, functions of two random variables, and Gaussian random variables.

Covers the normal distribution, including its properties and calculations.

Introduces random variables and their significance in information theory, covering concepts like expected value and Shannon's entropy.