Variable-order Bayesian network (VOBN) models provide an important extension of both the
Bayesian network
A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bay ...
models and the
variable-order Markov models. VOBN models are used in
machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
in general and have shown great potential in
bioinformatics
Bioinformatics () is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combi ...
applications.
These models extend the widely used
position weight matrix
A position weight matrix (PWM), also known as a position-specific weight matrix (PSWM) or position-specific scoring matrix (PSSM), is a commonly used representation of motifs (patterns) in biological sequences.
PWMs are often derived from a set ...
(PWM) models,
Markov models, and Bayesian network (BN) models.
In contrast to the BN models, where each random variable depends on a fixed subset of random variables, in VOBN models these subsets may vary based on the specific realization of observed variables. The observed realizations are often called the context and, hence, VOBN models are also known as context-specific Bayesian networks.
[{{cite conference , title=Context-specific independence in Bayesian networks , last1=Boutilier , first1=C. , last2=Friedman , first2=N. , author-link2=Nir Friedman , last3=Goldszmidt , first3=M. , last4=Koller , first4=D., author-link4=Daphne Koller , date=1996 , publisher= , book-title= , pages=115–123 , location=Reed College, Portland, Oregon, USA , conference=12th Conference on Uncertainty in Artificial Intelligence (August 1–4, 1996) , id= , url = http://www.informatik.uni-trier.de/~ley/db/conf/uai/uai1996.html]
The flexibility in the definition of conditioning subsets of variables turns out to be a real advantage in classification and analysis applications, as the statistical dependencies between random variables in a sequence of variables (not necessarily adjacent) may be taken into account efficiently, and in a position-specific and context-specific manner.
See also
*
Markov chain
A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. Informally, this may be thought of as, "What happe ...
*
Examples of Markov chains
*
Variable order Markov models
*
Markov process
A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. Informally, this may be thought of as, "What happe ...
*
Markov chain Monte Carlo
In statistics, Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a Markov chain that has the desired distribution as its equilibrium distribution, one can obtain ...
*
Semi-Markov process
In probability and statistics, a Markov renewal process (MRP) is a random process that generalizes the notion of Markov jump processes. Other random processes like Markov chains, Poisson processes and renewal processes can be derived as special ...
*
Artificial intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech re ...
References
External links
*VOMBAT: https://www2.informatik.uni-halle.de:8443/VOMBAT/
Bayesian networks
Markov models