# 베이즈 네트워크

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## 노트[편집]

### 위키데이터[편집]

- ID : Q812540

### 말뭉치[편집]

- A Bayesian network is a type of graph called a Directed Acyclic Graph or DAG.
^{[1]} - Online learning (also known as adaptation) with Bayesian networks, enables the user or API developer to update the distributions in a Bayesian network each record at a time.
^{[1]} - Things that we know (evidence) can be set on each node/variable in a Bayesian network.
^{[1]} - Bayes net model describing the performance of a student on an exam.
^{[2]} - Thus, a Bayesian network defines a probability distribution \(p\).
^{[2]} - This raises the question: which independence assumptions are we exactly making by using a Bayesian network model with a given structure described by \(G\)?
^{[2]} - For simplicity, let’s start by looking at a Bayes net \(G\) with three nodes: \(A\), \(B\), and \(C\).
^{[2]} - Bayesian network is a type of PGM that allows one to capture causal information (cause and effect) using directed edges (Kohler and Friedman, 2009; Gershman and Blei, 2012).
^{[3]} - Feng and Xie (2012) provided an algorithm for merging expert knowledge and information stored in databases into a single Bayesian network.
^{[3]} - In Section 3, we will present novel ideas for using Bayesian network models for anomaly detection and root-cause analysis in CPSs with unlabeled data.
^{[3]} - Bayesian network is an increasingly popular method in modeling uncertain and complex problems, because its interpretability is often more useful than plain prediction.
^{[4]} - The Bayesian network classifiers were trained with a hill-climbing searching for the qualified network structure and parameters measured by maximum description length.
^{[4]} - The Bayesian network with 50 features filtered by information gain can predict 3-month functional independence with an AUC of 0.875 and 1-year mortality with an AUC of 0.895.
^{[4]} - The Bayesian network, a machine learning method, predicts and describes classification based on the Bayes theorem (14).
^{[4]} - For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms.
^{[5]} - This situation can be modeled with a Bayesian network (shown to the right).
^{[5]} - Using a Bayesian network can save considerable amounts of memory over exhaustive probability tables, if the dependencies in the joint distribution are sparse.
^{[5]} - In the simplest case, a Bayesian network is specified by an expert and is then used to perform inference.
^{[5]} - And yet from a Bayesian network, every entry in the full joint distribution can be easily calculated, as follows.
^{[6]} - The Bayes net for the problem is shown fleshed out below.
^{[6]} - A small example Bayesian network structure for a (somewhat facetious/futuristic) medical diagnostic domain is shown below.
^{[7]} - Once a Bayesian network has been specified, it may be used to compute any conditional probability one wishes to compute.
^{[7]} - The BDe score is proportional to the posterior probability of a Bayesian network structure given the data and it has the event equivalence property.
^{[8]} - That is, two Bayesian network structures that represent the same set of independence assertions have equal BDe scores.
^{[8]} - This paper proposes a method based on Bayesian network for custom products and establishes a framework for product preparation methods under incomplete requirements.
^{[9]} - In this paper, a tree-shaped Bayesian network is adopted.
^{[9]} - The advantage of being able to use this tree-shaped Bayesian network structure is that it can greatly improve the speed of reasoning and improve the efficiency of product configuration when reasoning.
^{[9]} - A Bayesian network is drawn as a graph, with nodes and edges.
^{[10]} - In a Bayesian network, dependence is indicated by directed edges.
^{[10]} - A good source to learn more about Bayesian networks, and Bayesian network inference algorithms, is B-Course, developed at the University of Helsinki.
^{[10]} - Banjo is a Bayesian network inference algorithm developed by my collaborator, Alexander Hartemink at Duke University.
^{[10]} - next → ← prev Bayesian Belief Network in artificial intelligence Bayesian belief network is key computer technology for dealing with probabilistic events and to solve a problem which has uncertainty.
^{[11]} - We can define a Bayesian network as: "A Bayesian network is a probabilistic graphical model which represents a set of variables and their conditional dependencies using a directed acyclic graph.
^{[11]} - " It is also called a Bayes network, belief network, decision network, or Bayesian model.
^{[11]} - Real world applications are probabilistic in nature, and to represent the relationship between multiple events, we need a Bayesian network.
^{[11]} - They are available in different formats from several sources, the most famous one being the Bayesian network repository hosted at the Hebrew University of Jerusalem.
^{[12]} - A BN (Bayes Net) model was also trained.
^{[13]} - In a Bayesian network, a variable takes on values from a collection of mutually exclusive and collective exhaustive states.
^{[14]} - Each of these factorizations can be represented by a Bayesian network.
^{[15]} - Next, we show how the stochastic devices described in the previous section can be used to implement a two node Bayesian network in hardware.
^{[16]} - BNFinder or Bayes Net Finder is an open-source tool for learning Bayesian networks written purely in Python.
^{[17]} - This Java toolkit is mainly used for training, testing, and applying Bayesian Network Classifiers.
^{[17]} - It also provides a good list of search algorithms for learning Bayesian network structures.
^{[18]}

### 소스[편집]

- ↑
^{1.0}^{1.1}^{1.2}Introduction to Bayesian networks - ↑
^{2.0}^{2.1}^{2.2}^{2.3}Bayesian networks - ↑
^{3.0}^{3.1}^{3.2}Bayesian Networks - an overview - ↑
^{4.0}^{4.1}^{4.2}^{4.3}A Bayesian Network Model for Predicting Post-stroke Outcomes With Available Risk Factors - ↑
^{5.0}^{5.1}^{5.2}^{5.3}Bayesian network - ↑
^{6.0}^{6.1}Artificial Intelligence > Bayesian Nets (Stanford Encyclopedia of Philosophy) - ↑
^{7.0}^{7.1}What are Bayesian Networks? - ↑
^{8.0}^{8.1}Applications of Bayesian network models in predicting types of hematological malignancies - ↑
^{9.0}^{9.1}^{9.2}Bayesian Network Approach to Customer Requirements to Customized Product Model - ↑
^{10.0}^{10.1}^{10.2}^{10.3}Bayesian Networks - ↑
^{11.0}^{11.1}^{11.2}^{11.3}Bayesian Belief Network in artificial intelligence - ↑ Bayesian Network Repository
- ↑ Multimodal Bayesian Network for Artificial Perception
- ↑ Communications of the ACM
- ↑ Bayesian Networks – BayesFusion
- ↑ Hardware implementation of Bayesian network building blocks with stochastic spintronic devices
- ↑
^{17.0}^{17.1}Top 8 Open Source Tools For Bayesian Networks - ↑ Bayesian Networks With Examples in R

## 메타데이터[편집]

### 위키데이터[편집]

- ID : Q812540