Point Estimation 2 – ML
Introduction This notes confines to MLE in obtaining Point Estimators for parameters Suggested Reading: [CABE] Casella, G., & Berger, R.
Frequentist, YES
YES
Frequentist, YES
Point Estimation 1 – MoM
Introduction This notes confines to MOM in obtaining Point Estimators for parameters Suggested Reading: [CABE] Casella, G., & Berger, R.
Frequentist, YES
Statistical Inference Preliminaries
Introduction This notes provides few ideas of sampling distribution of sample mean and variance Suggested Reading: [CABE] Casella, G., &
Bayesian, YES
Sequential Updating
Bayesian Tool Kit In Bayesian analysis, sequential updating refers to the process of continuously updating beliefs about a parameter as
Bayesian, YES
Normal Models – Two Parameters Case
Normal Model: Both $\mu$ and $\sigma^2$ are unknown In the one-parameter models, only $\mu$ or only $\sigma^2$ is unknown, so
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