# AIMA: Probabilistic Reasoning

> Source: https://ollybritton.com/notes/textbooks/ai-a-modern-approach/uncertain-knowledge-and-reasoning/probabilistic-reasoning/ · Updated: 2021-04-04 · Tags: aima, notes

> In which we explain how to build efficient network models to reason under uncertainty according to the laws of probability theory, and how to distinguish between correlation and causality.

## Flashcards
##### What is a Bayesian network??
An efficient representation of full joint probability distributions that contain information about the relationships between random variables.

##### Informally, what is a DAG??
A directed tree but nodes are allowed to have multiple parents.

##### In a Bayesian network, what does $X \to Y$ represent??
$X$ has a direct effect on $Y$.

##### What is a hybrid Bayesian network??
A Bayes net containing both discrete and continuous variables.

##### Why is exact inference in Bayesian networks exponential in the worst case??
Because you can encode SAT problems into Bayesian networks.

##### What two things does a query to a Bayesian network contain ??
* A set of query variables, probabilities you're asking for.
* A set of evidence variables.

##### How can you make approximate inference in Bayesian networks 2-3 OOM faster??
Compile the Bayesian network into a small program.

##### What is a causal network??
Bayesian networks where only causal relationships are allowed.

##### What is the benefit of using a causal network rather than a Bayesian network??
You can predict the effects of interventions.

---
Olly Britton — https://ollybritton.com. Machine-readable index: https://ollybritton.com/llms.txt