Mathematics for AI and Machine Learning

Foundations for modern AI and machine learning

Partition Function

Chapter 17 Probability & information

Chapter 17: Score Function and Energy-Based Models — Partition Function Z

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From the book

Chapter 17: Score Function and Energy-Based Models. In the chapter mind map this icon labels Partition Function Z. The discussion below is excerpted and lightly edited from § Partition Function Z in Mathematics for AI and Machine Learning.

Let $\mathbf{x} \in \mathbb{R}^d$ be a continuous random variable with density $p(\mathbf{x})$. The score function is defined as

What this drawing shows

What you see. Integration domain $\int e^{-E}\,dx$ fills in while $Z$ stays intractable, highlighting normalization of energy-based models.

In the mind map. Chapter 17 — Partition Function Z. See From the book above for definitions, figures, and worked examples.

Where to read next

Open Chapter 17 companion →

Read the full definitions, figures, and worked examples in Chapter 17: Score Function and Energy-Based Models — see the mind-map node Partition Function Z.