MullOverThings

Useful tips for everyday

# What are Bayesian networks used for?

## What are Bayesian networks used for?

Bayesian networks are a type of Probabilistic Graphical Model that can be used to build models from data and/or expert opinion. They can be used for a wide range of tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction and decision making under uncertainty.

## What is special about Bayesian networks?

Bayesian networks aim to model conditional dependence, and therefore causation, by representing conditional dependence by edges in a directed graph. Through these relationships, one can efficiently conduct inference on the random variables in the graph through the use of factors.

## What is the difference between Markov chain and Markov process?

A Markov chain is a discrete-time process for which the future behaviour, given the past and the present, only depends on the present and not on the past. A Markov process is the continuous-time version of a Markov chain. Many queueing models are in fact Markov processes.

## How can you tell if a chain is Markov?

Markov Chains: A discrete-time stochastic process X is said to be a Markov Chain if it has the Markov Property: Markov Property (version 1): For any s, i0,…,in−1 ∈ S and any n ≥ 1, P(Xn = s|X0 = i0,…,Xn−1 = in−1) = P(Xn = s|Xn−1 = in−1).

## What’s the difference between Bayesian networks and Markov networks?

, Have been working in Machine Learning for a few years. As explained in the other answer, a Bayesian network is a directed graphical model, while a Markov network is an undirected graphical model, and they can encode different set of independence relations.

## Which is the best description of a Bayesian network?

A Bayesian network, Bayes network, belief network, Bayes (ian) model or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model) that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG).

## Which is a weakness of a Markov process?

The main weakness of Markov networks is their inability to represent induced and non-transitive dependencies; two independent variables will be directly connected by an edge, merely because some other variable depends on both. As a result, many useful independencies go unrepresented in the network.

## What is the difference between a Markov network and a PGMs?

The “Markov” in “Markov network” refers to a generic notion of conditional independence encoded by PGMs, that of a set of random variables x A being independent of others x C given some set of “important” variables x B (the technical name is a Markov blanket ), i.e. p ( x A | x B, x C) = p ( x A | x B).