Lectures related to Beta function

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Probability and Statistics: Fundamental Theorems

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Green's Function: Theory and Applications

Covers the theory and applications of Green's function in solving differential equations.

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Linear Approximation and Derivative Parametric

Covers linear approximation, parametric derivatives, and differentiability conditions on intervals.

Fourier Series: Understanding Periodicity and Coefficients

Explores t-periodic functions in Fourier series, discussing intervals, propositions, and variable changes for coefficient calculation and series convergence.

Bayesian Inference: Beta-Bernoulli Model

Explores the Beta distribution, Bayesian inference, and posterior calculation in the Beta-Bernoulli model.

Gamma Function and Stirling's Approximation: Mathematical Methods

Discusses the gamma function, its properties, and Stirling's approximation for large factorials, emphasizing their significance in mathematical methods for physics.

Reference Flux: Key Concepts

Explores the concept of reference flux and key parameters in product sustainability.

Fundamental Solutions of Laplace Equation

Covers the fundamental solutions of the Laplace equation and introduces distributions.

Taylor Series: Convergence and Applications

Explores Taylor series convergence and applications in approximating functions and solving mathematical problems.

Construction of Solutions by Dirichlet-Laplace

Explores the construction of solutions by Dirichlet-Laplace in general domains.

Understanding 2D Adjoint QCD

Explores the beta functions of 2D adjoint QCD and the implications of adding four Fermi terms to the action.

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Uniqueness Results for Poisson Equation

Explores uniqueness results for the Poisson equation and harmonic functions with different boundary conditions.

Exploration Bias

Explores regularization, learning algorithms, and subgaussian assumptions in machine learning.

Differential Calculus: Definition and Derivability

Explores the definition and derivability of functions in differential calculus, emphasizing differentiability at specific points.

Theory of Scalar Fields

Covers the theory of scalar fields, stability of fixed points, eigenvalues, Lagrangian interactions, tensors, and beta functions.

Functions in Python

Introduces functions in Python, covering predefined and user-defined functions, formal and effective parameters, and the importance of docstrings.

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