# 그래프 모형

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## 노트

### 위키데이터

- ID : Q1143367

### 말뭉치

- In recent years, the L-1 regularization has been extensively used to estimate a sparse precision matrix and encode an undirected graphical model.
^{[1]} - -This module provides an overview of graphical model representations and some of the real-world considerations when modeling a scenario as a graphical model.
^{[2]} - We then introduce variational methods, which exploit laws of large numbers to transform the original graphical model into a simplified graphical model in which inference is efficient.
^{[3]} - With the proposed method, we utilize a collective graphical model with which we can learn individual transition models from the aggregated data by analytically marginalizing the individual locations.
^{[4]} - Learning a spatio-temporal collective graphical model only from the aggregated data is an ill-posed problem since the number of parameters to be estimated exceeds the number of observations.
^{[4]} - A graphical model is a probabilistic model for which a graph denotes the conditional independence structure between random variables.
^{[5]} - Now, the key goal from learning a probabilistic graphical model is to learn the ‘Joint probability distribution’ represented by P(X1, X2, ..Xn) for a set of random variables.
^{[6]} - It is beyond the scope of this paper to describe the technical aspects of the Gaussian graphical model in detail, readers are guided to Epskamp et al.
^{[7]} - Illustrating the estimation of a Gaussian graphical model using the extended Bayesian information criteria (EBIC) and the glasso algorithm.
^{[7]} - Gaussian graphical model after applying the glasso algorithm with 4 tuning parameter values.
^{[7]} - The Gaussian graphical model differs from typical exploratory analysis based on partial correlational coefficients.
^{[7]} - From a statistical point of view, we can think of a phylogenetic tree as a graphical model .
^{[8]} - First, the use of restricted graphical model relies on the minimum-spanning-tree, which has been introduced in Sect.
^{[9]} - This type of graphical model is known as a directed graphical model, Bayesian network, or belief network.
^{[10]} - Fundamental to the idea of a graphical model is the notion of modularity -- a complex system is built by combining simpler parts.
^{[11]} - are special cases of the general graphical model formalism -- examples include mixture models, factor analysis, hidden Markov models, Kalman filters and Ising models.
^{[11]} - The graphical model framework provides a way to view all of these systems as instances of a common underlying formalism.
^{[11]} - A graphical model is a way to represent a joint multivariate probability distribution as a graph.
^{[12]} - In a graphical model, the nodes represent variables and the edges represent conditional dependencies among the variables.
^{[12]} - Nearly any probabilistic model can be represented as a graphical model: neural networks, classification models, time series models, and of course phylogenetic models!
^{[12]} - To demonstrate how to use the Rev language to specify a graphical model, we will start with a simple non-phylogenetic model.
^{[12]} - In a graphical model, variables are represented by a set of nodes and their associated interactions are represented by edges.
^{[13]} - Before talking about how to apply a probabilistic graphical model to a machine learning problem, we need to understand the PGM framework.
^{[14]} - Formally, a probabilistic graphical model (or graphical model for short) consists of a graph structure.
^{[14]}

### 소스

- ↑ Graphical Models in Financial Market and Portfolio Allocation: Applications and Considerations
- ↑ Free Online Course: Probabilistic Graphical Models 1: Representation from Coursera
- ↑ An Introduction to Variational Methods for Graphical Models
- ↑
^{4.0}^{4.1}Neural Collective Graphical Models for Estimating Spatio-Temporal Population Flow from Aggregated Data - ↑ CRAN Task View: gRaphical Models in R
- ↑ PGM 1: Introduction to Probabilistic Graphical Models
- ↑
^{7.0}^{7.1}^{7.2}^{7.3}Using a Gaussian Graphical Model to Explore Relationships Between Items and Variables in Environmental Psychology Research - ↑ Graphical Model - an overview
- ↑ Graphical Model - an overview
- ↑ Graphical model
- ↑
^{11.0}^{11.1}^{11.2}Graphical Models - ↑
^{12.0}^{12.1}^{12.2}^{12.3}RevBayes: Introduction to Graphical Models - ↑ Bayesian graphical models for computational network biology
- ↑
^{14.0}^{14.1}Probabilistic Graphical Models Tutorial — Part 1

## 메타데이터

### 위키데이터

- ID : Q1143367