Mathematics

Cramer's rule, explained geometrically | Chapter 12, Essence of linear algebra

Fermat, Mersenne, factorisation et nombres parfaits

Fermat, Mersenne, factorization and perfect numbers

But why is a sphere's surface area four times its shadow?

Using topology for discrete problems | The Borsuk-Ulam theorem and stolen necklaces

What are divergent iterations and what to do about them?

How to write your first Stan program

Visualizing the 4d numbers Quaternions

Q&A with Grant Sanderson (3blue1brown)

Why slicing a cone gives an ellipse (beautiful proof)

The other way to visualize derivatives | Chapter 12, Essence of calculus

Introducing Bayes factors and marginal likelihoods

Why we typically use dependent sampling to sample from the posterior

What is meant by entropy in statistics?

What is a posterior predictive check and why is it useful?

The problems with using simple Monte Carlo to determine the marginal likelihood

The problem with discrete approximation to integrals or probability densities

The intuition behind the Hamiltonian Monte Carlo algorithm

How to do integration by sampling

How to derive a Gibbs sampling routine in general

Explaining the difference between confidence and credible intervals

Estimating the posterior predictive distribution by sampling

Effective sample size: representing the cost of dependent sampling

Constrained parameters? Use Metropolis-Hastings

An introduction to the Random Walk Metropolis algorithm

An introduction to the Poisson distribution - 2

An introduction to the gamma distribution

An introduction to the Beta distribution

An introduction to rejection sampling

An introduction to reference priors

An introduction to numerical integration through Gaussian quadrature

An introduction to Jeffreys priors - 2

An introduction to inverse transform sampling

An introduction to importance sampling - optimal importance distributions

An introduction to importance sampling

An introduction to Gibbs sampling

An introduction to continuous marginal probability distributions

Evaluating model fit through AIC, DIC, WAIC and LOO-CV

Lecture 18 | MIT 6.832 (Underactuated Robotics), Spring 2018

The syllabus covered by the book and YouTube course

The Wallis product for pi, proved geometrically