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).