Sets & Functions – A Primer
Definitions and Notations A set is a well-defined, unordered collection of distinct objects. These objects are also called elements of
Sets and Functions, YEM
R, SEE
GLM Algorithm and R Libraries
Choosing the Right Link Function Generalised Linear Models (GLM) offer a flexible framework for regression analysis. When building a GLM
Applications, SEE
Transformation in GLM
Variable Treatment and Coefficient Interpretation in Logistic Regression A Case Study Using the Bank Marketing Dataset About the Data The
Python, SEE
GLM Algorithms and Python Libraries
SKlearn and Statsmodels Choosing the Right Link Function Generalised Linear Models (GLM) offer a flexible framework for regression analysis. When
Bayesian, YES
Normal – Inverse Gamma and Normal-Gamma Models
Normal-Inverse Gamma Model for Variance Assumptions Approach A: Normal — Inverse Gamma Step 1. The Likelihood $$y_i \mid \sigma^2 \sim
Bayesian, YES
Normal-Normal Conjugate Model – 2
Approach B: Via the Full Joint Likelihood Recall from Approach AThe Normal-Normal model is treated via two approaches — through
Bayesian, YES
Normal-Normal Conjugate Model – 1
These notes develop the Bayesian conjugate analysis for Normal-Normal — following a unified five-step approach: The Normal-Normal model is treated
Bayesian, YES
Poisson-Gamma Conjugate Model
1. The Likelihood Function We observe $n$ independent data points $y_1, y_2, \dots, y_n$ where each $y_i$ follows a Poisson
Bayesian, YES
Quick Bites-Bayesian Inference
This Quick Bites is a beginner-friendly primer on Bayesian Inference, carefully scoped to build intuition before formalism. It opens with
Bayesian
Predictive Distributions
Bayesian Tool Kit The posterior predictive distribution is a critical concept in Bayesian inference, providing a way to predict future