Bayesian network

(noun)

A probabilistic model that represents a set of random variables and their conditional dependencies (e.g., a Bayesian network could calculate the probabilities between symptoms and a disease).

Related Terms

Examples of Bayesian network in the following topics:

  • Evaluate Alternatives

    • Model potential decision alternatives through utilizing pro/con analysis, influence diagrams, decision trees and Bayesian networks

  • Bayes' Rule

    • This is known as Bayesian inference, which is fundamental to Bayesian statistics.
    • Bayes' rule is widely used in statistics, science and engineering, such as in: model selection, probabilistic expert systems based on Bayes' networks, statistical proof in legal proceedings, email spam filters, etc.
    • Bayesian updating is an important technique throughout statistics, and especially in mathematical statistics.
    • Bayesian updating is especially important in the dynamic analysis of a sequence of data.
    • This procedure is termed Bayesian updating.

  • What Is a Confidence Interval?

    • Bayesian inference provides further answers in the form of credible intervals.
    • Ostensibly, the Bayesian approach offers intervals that (subject to acceptance of an interpretation of "probability" as Bayesian probability) offer the interpretation that the specific interval calculated from a given dataset has a certain probability of including the true value (conditional on the data and other information available).

  • Types of Networks

    • A storage area network (SAN) is a dedicated network that provides access to consolidated, block level data storage.
    • A campus area network (CAN) is a computer network made up of an interconnection of LANs within a limited geographical area.
    • A backbone network is part of a computer network infrastructure that interconnects various pieces of network, providing a path for the exchange of information between different LANs or subnetworks.
    • Network performance management, including network congestion, are critical parameters taken into account when designing a network backbone.
    • Backbone networks are similar to enterprise private networks.

  • Routing

    • Routing is the process of selecting paths in a network along which to send network traffic.
    • Routing is the process of selecting paths in a network along which to send network traffic.
    • Routing is performed for many kinds of networks, including the telephone network (circuit switching), electronic data networks (such as the internet), and transportation networks.
    • A transport network, (or transportation network in American English), is typically a network of roads, streets, pipes, aqueducts, power lines, or nearly any structure which permits either vehicular movement or flow of some commodity.
    • A transport network may combine different modes of transport.

  • Estimating the Target Parameter: Interval Estimation

  • Social Networks

    • Facebook is an example of a large social network.
    • Social networks are composed of nodes and ties.
    • Smaller, tighter networks composed of strong ties behave differently than larger, looser networks of weak ties.
    • The study of social networks is called either social network analysis or social network theory.
    • Assess the role of social networks in the socialization of people

  • Variation and Prediction Intervals

    • A prediction interval bears the same relationship to a future observation that a frequentist confidence interval or Bayesian credible interval bears to an unobservable population parameter.
    • Alternatively, in Bayesian terms, a prediction interval can be described as a credible interval for the variable itself, rather than for a parameter of the distribution thereof.

  • Modality and levels of analysis

    • The network analyst tends to see individual people nested within networks of face-to-face relations with other persons.
    • Often these networks of interpersonal relations become "social facts" and take on a life of their own.
    • A family, for example, is a network of close relations among a set of people.
    • Most social network analysts think of individual persons as being embedded in networks that are embedded in networks that are embedded in networks.
    • In chapter 17, we'll take a look at some methods for multi-mode networks.

  • Network Models of Memory

    • Network models are based on the concept of connectionism.
    • There are several types of network models in memory research.
    • Some define the fundamental network unit as a piece of information.
    • However, network models generally agree that memory is stored in neural networks and is strengthened or weakened based on the connections between neurons.
    • PDP posits that memory is made up of neural networks that interact to store information.