Sequential Updating
Bayesian Tool Kit In Bayesian analysis, sequential updating refers to the process of continuously updating beliefs about a parameter as
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Bayesian
All about bayesian thinking
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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
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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
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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
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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
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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
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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
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Predictive Distributions
Bayesian Tool Kit The posterior predictive distribution is a critical concept in Bayesian inference, providing a way to predict future
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A Note about Prior Distribution
In Bayesian analysis, the prior distribution plays a crucial role, as it treats parameters as random variables. The selection of
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Bayesian Estimation_1
Before you proceed, please refer to Bayes theorem and inferential preliminaries Introduction to Bayesian Approaches for Statistical Inference Bayesian statistics