Mathematics

A pretty reason why Gaussian + Gaussian = Gaussian

This pattern breaks, but for a good reason | Moser's circle problem

Convolutions | Why X+Y in probability is a beautiful mess

Soundness Spectra

What happens if you just keep squaring?

On the (non)existence of proof sytems in universal proof theory

6.8210 Spring 2023 Lecture 20: Stochastic dynamics

Estimation d'un paramètre

Estimating a parameter

Why π is in the normal distribution (beyond integral tricks)

But what is the Central Limit Theorem?

Cours 18 - Modèles en chaînes de Markov

Spring 2023 6.8210 Lecture 2: Nonlinear Dynamics

Cours 13 - Modèles de groupes

Cours 12 - Hétérogénéité de groupe et spatiale

Cours 11 - Quelques curiosités et modèles en plus

Cours 10 - Plus de modèles épidémiques

Parameter identification (fitting a model) and identifiability

Numerics with models for ETP

The Art of Ordinal Analysis

Régression logistique. 1 ) Principe

Logistic Regression. 1) Principle

Régression logistique. 2) Pratique

Logistic Regression. 2) Practical Application

Essential Hereditary Undecidability

Permutations and Combinations - BSc Physics Series - by Shilpy Bhullar (Hindi/English)

Green's Theorem - BSc Physics Series - by Shilpy Bhullar (Hindi/English)

Cours 05 - Fonctions d'incidence

Bayes' theorem

Multi-Dimensional Gaussian Probability Density Functions

Cours 07 - Étapes de l'analyse mathématique des modèles, R0 et la taille finale d'une épidémie

Cours 06 - Le modèle de Kermack et McKendrick

Cours 14 - Modèles dans des métapopulations

Cours 04 - Le modèle SIS

Normal forms of proofs in natural deduction II: complexity

But what is a convolution?

Normal forms of proofs in natural deduction I: existence and uniqueness

Researchers thought this was a bug (Borwein integrals)

Qiskit FalllFest CIC-IPN Mexico 2022- Combinatorial optimisation through OpenQAOA

Introduction to Quadratic Programming

History of Mathematical Logic in China: From its beginnings to Tsinghua University

Organisation of the course and brief introduction to Mathematical Epidemiology

Introduction to Mathematical Epidemiology: the SIS and Kermack and McKendrick epidemiological models