# Notes - Uncertainty in Deep Learning MT25, Bayesian probability theory

> Source: https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/lectures/bayesian-probability-theory/ · Updated: 2025-11-01 · Tags: uni, lecture

- [Course - Uncertainty in Deep Learning MT25](https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/)
	- Previous lecture: [Lecture - Uncertainty in Deep Learning MT25, Introduction](https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/lectures/introduction/)
	- Next lecture: [Lecture - Uncertainty in Deep Learning MT25, Bayesian probabilistic modelling](https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/lectures/modelling/)
	- [Notes - Probability MT22, Axioms of probability space](https://ollybritton.com/notes/uni/prelims/mt22/probability/notes/axioms-of-probability-space/)
	- ["Dutch book theorems"](https://en.wikipedia.org/wiki/Dutch_book_theorems) on Wikipedia

This lecture derived the laws of probability theory from the requirements for a set of beliefs to be rational. The lecture proved this: suppose

- $X$ is a sample space of events, i.e. a set of possible outcomes
- $A \subseteq X$ is an event, which is a subset of $X$
- $b_A$ is your "belief" in event $A$, defined as the price you would be willing to buy or sell a unit wager that $A$ happens. This means $\{ b_A \mid A \subseteq X\}$ assigns a number to each subset of $X$.

Then:

- $\{ b_A \}_{A \subseteq X}$ are a set of rational beliefs if and only if $\mathbb P(A) := b_A$ satisfies the laws of probability theory

What does it mean for a set of beliefs to be rational? Roughly, it means that there is no Dutch book against that set of wagers; i.e. a sequence of purchases and sells of unit those unit wagers that means the bookkeeper always lose money.

What are the laws of probability theory? Roughly, $\{ b_A \}_{A \subseteq X}$ is a set of rational beliefs if:

- $0 \le b_A \le 1$ for all $A \subseteq X$
- $b_X = 1$
- Two disjoint events satisfy $b_{A \cup B} = b_A + b_B$ (in fact, you need to extend this to countable additivity)

For ML models to be rational, they need to be obey the laws of probability theory.

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