China
Africa
Explore chapters and articles related to this topic
Machine Learning
Published in Seyedeh Leili Mirtaheri, Reza Shahbazian, Machine Learning Theory to Applications, 2022
Seyedeh Leili Mirtaheri, Reza Shahbazian
A Bayesian network [48], belief network, also called directed acyclic graphical model, is a probabilistic graphical model. In this graph, a set of random variables and their conditional independence is represented with a directed acyclic graph (DAG). For example, a Bayesian network can be a graph showing the probabilistic relationships between diseases and symptoms. We can calculate the probabilities of the existence of different diseases given symptoms. Some practices implement inference and learning with a good performance. A dynamic Bayesian network is a Bayesian network that models sequences of variables, like speech signals or protein sequences. Influence diagrams a generalized Bayesian network that represents and solves uncertain decision-making problems.
Nonmyopic Sensor Management
Published in David L. Hall, Chee-Yee Chong, James Llinas, Martin Liggins, Distributed Data Fusion for Network-Centric Operations, 2013
Bayesian object classification uses the generative approach discussed in Section 9.2. We use the Bayesian network formalism to represent the model relating the object class to the sensor measurements. A Bayesian network (Jensen 2001, Pearl 1988) is a graphical representation of a probabilistic model. It is a directed graph where the nodes represent random variables or random vectors of interest and the directed links represent probabilistic relationships between the variables. A dynamic Bayesian network is one in which some of the nodes are indexed by time. A key feature of Bayesian networks is the explicit representation of conditional independence needed for optimal distributed object classification.
Fault diagnosis of offshore wind turbine gearboxes using a dynamic Bayesian network
Published in International Journal of Sustainable Energy, 2022
Tobiloba Elusakin, Mahmood Shafiee
Dynamic Bayesian networks (DBN) are an extension of BNs which include an explicit time dimension. The DBN approach expands the power of conventional reliability analyses by including common causes and multi-state variables (Ashrafi and Zadeh 2017). A DBN is a transition model based on random probability distribution splintered over a set of random variables, over which a collection of conditional probability assumptions is defined. The feature of time-invariance also ensures that the variable dependency model remains unchanged at any time. The term ‘dynamic' is adopted as an attachment to BN when multiple time slices are needed to accurately represent the evolution of the system through time (Montani et al. 2008). Due to a DBN being analogous to a semi-Markovian random process with order , a local BN is extended to t number of time slices when . Such models are referred to as ‘two-time slice temporal Bayesian network' or 2-TBN (Montani et al. 2008). A general structure of a DBN is shown in Figure 3.
Learning Dynamic Bayesian Networks structure based on a new hybrid K2-Bat learning algorithm
Published in Journal of the Chinese Institute of Engineers, 2021
Yu-Jing Deng, Hao-Ran Liu, Hai-Yu Wang, Bin Liu
Dynamic Bayesian Network is the extension of Bayesian Network in time series. Based on probability theory and graph theory, DBN combines time dimension with static Bayesian Network to build a dynamic reasoning model that can dynamically analyze and predict time information. It can effectively build the dependence between variables that change with time, and can be used to represent the properties of complex stochastic processes and to predict and reason. DBN learning can be divided into network structure learning and network reasoning analysis, and structure learning is the core content of DBN learning.
Big Data-based Human Resource Performance Evaluation Model Using Bayesian Network of Deep Learning
Published in Applied Artificial Intelligence, 2023
The dynamic Bayesian network (DBN) mainly relates the static Bayesian network with time, that is, it extends the static Bayesian network in time. Dynamic Bayesian network is a random network model which can process real-time data based on probability distribution. By adding time elements to the static Bayesian network, not only the random variable data in the Bayesian network can be updated in real time, but also the network structure and parameters are constantly changing. Therefore, dynamic Bayesian network can be better used to deal with real-time data and hierarchical knowledge expression.