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✅ Topic 26: Bayesian Inference

Course: AI Math Advanced Topic: Bayesian Inference Section: 3 — Probability Theory Level:…

Overview

✅ Topic 26: Bayesian Inference — Quick Facts

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D) ∝ P(D: θ)·P(θ)

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- MAP = argmax P(θ: D) = MLE with regularization

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- Predictive Distribution = P(x_new: D) = ∫P(x

Topic 1

📘 Overview

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Posterior = P(θ|D) ∝ P(D|θ)·P(θ)

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Prior = P(θ) initial belief

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MAP = argmax P(θ|D) = MLE with…

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Conjugate Priors =…

Topic 2

🏗️ Bayesian Inference Architecture

💡 ` ┌──────────────────────────────────────────────────┐ │ BAYESIAN INFERENCE ENGINE │ │ │…

Topic 3

🧠 Python Implementation

🎯 `python """ Bayesian Inference — Prior-posterior update, conjugate pairs, MAP estimation,…

Topic 4

❓ Quiz — 5 MCQs

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A) Same hain

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B) MAP me prior bhi consider hota…

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C) MAP always worse

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D) MLE uses prior

Topic 5

🔗 Navigation

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Quick Quiz

Quiz — Question 1

✅ Topic 26: Bayesian Inference ka sabse sahi definition kya hai?

Quick Quiz

Quiz — Question 2

✅ Topic 26: Bayesian Inference ka '- MAP = argmax P(θ' kya hai?

Complete! 🎉

✅ Topic 26: Bayesian Inference Complete!

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