A Study on Comparison of Bayesian Network Structure Learning Algorithms for Selecting Appropriate Models with BNDataGenerator in R
Reference : Jae-seong Yoo, (2014), "A Study on Comparison of Bayesian Network Structure Learning Algorithms for Selecting Appropriate Models", M.S. thesis, Department of Statistics, Korea University, Seoul.
An Image representation using Compressive Sensing and Arithmetic Coding IJCERT
The demand for graphics and multimedia communication over intenet is growing day by day. Generally the coding efficiency achieved by CS measurements is below the widely used wavelet coding schemes (e.g., JPEG 2000). In the existing wavelet-based CS schemes, DWT is mainly applied for sparse representation and the correlation of DWT coefficients has not been fully exploited yet. To improve the coding efficiency, the statistics of DWT coefficients has been investigated. A novel CS-based image representation scheme has been proposed by considering the intra- and inter-similarity among DWT coefficients. Multi-scale DWT is first applied. The low- and high-frequency subbands of Multi-scale DWT are coded separately due to the fact that scaling coefficients capture most of the image energy. At the decoder side, two different recovery algorithms have been presented to exploit the correlation of scaling and wavelet coefficients well. In essence, the proposed CS-based coding method can be viewed as a hybrid compressed sensing schemes which gives better coding efficiency compared to other CS based coding methods.
Naver learning to rank question answer pairs using hrde-ltcNAVER Engineering
The automatic question answering (QA) task has long been considered a primary objective of artificial intelligence.
Among the QA sub-systems, we focused on answer-ranking part. In particular, we investigated a novel neural network architecture with additional data clustering module to improve the performance in ranking answer candidates which are longer than a single sentence. This work can be used not only for the QA ranking task, but also to evaluate the relevance of next utterance with given dialogue generated from the dialogue model.
In this talk, I'll present our research results (NAACL 2018), and also its potential use cases (i.e. fake news detection). Finally, I'll conclude by introducing some issues on previous research, and by introducing recent approach in academic.
Ontological knowledge integration for Bayesian network structure learningUniversity of Nantes
Intégration de connaissances ontologiques pour l'apprentissage des réseaux bayésiens
5èmes Journées thématiques "Apprentissage Artificiel & Fouille de Données", 28 juin 2012, Université Paris 13, France.
(title in French, but slides english)
An Image representation using Compressive Sensing and Arithmetic Coding IJCERT
The demand for graphics and multimedia communication over intenet is growing day by day. Generally the coding efficiency achieved by CS measurements is below the widely used wavelet coding schemes (e.g., JPEG 2000). In the existing wavelet-based CS schemes, DWT is mainly applied for sparse representation and the correlation of DWT coefficients has not been fully exploited yet. To improve the coding efficiency, the statistics of DWT coefficients has been investigated. A novel CS-based image representation scheme has been proposed by considering the intra- and inter-similarity among DWT coefficients. Multi-scale DWT is first applied. The low- and high-frequency subbands of Multi-scale DWT are coded separately due to the fact that scaling coefficients capture most of the image energy. At the decoder side, two different recovery algorithms have been presented to exploit the correlation of scaling and wavelet coefficients well. In essence, the proposed CS-based coding method can be viewed as a hybrid compressed sensing schemes which gives better coding efficiency compared to other CS based coding methods.
Naver learning to rank question answer pairs using hrde-ltcNAVER Engineering
The automatic question answering (QA) task has long been considered a primary objective of artificial intelligence.
Among the QA sub-systems, we focused on answer-ranking part. In particular, we investigated a novel neural network architecture with additional data clustering module to improve the performance in ranking answer candidates which are longer than a single sentence. This work can be used not only for the QA ranking task, but also to evaluate the relevance of next utterance with given dialogue generated from the dialogue model.
In this talk, I'll present our research results (NAACL 2018), and also its potential use cases (i.e. fake news detection). Finally, I'll conclude by introducing some issues on previous research, and by introducing recent approach in academic.
Ontological knowledge integration for Bayesian network structure learningUniversity of Nantes
Intégration de connaissances ontologiques pour l'apprentissage des réseaux bayésiens
5èmes Journées thématiques "Apprentissage Artificiel & Fouille de Données", 28 juin 2012, Université Paris 13, France.
(title in French, but slides english)
Uncertainty Quantification with Unsupervised Deep learning and Multi Agent Sy...Bang Xiang Yong
Presented at MET4FOF Workshop, JULY 2020
I talk about our recent work of combining Bayesian Deep learning with Explainable Artificial Intelligence (XAI) methods. In particular, we look at Bayesian Autoencoders.
Inference of Nonlinear Gene Regulatory Networks through Optimized Ensemble of...Arinze Akutekwe
Comprehensive understanding of gene regulatory
networks (GRNs) is a major challenge in systems biology. Most
methods for modeling and inferring the dynamics of GRNs,
such as those based on state space models, vector autoregressive
models and G1DBN algorithm, assume linear dependencies
among genes. However, this strong assumption does not make
for true representation of time-course relationships across the
genes, which are inherently nonlinear. Nonlinear modeling
methods such as the S-systems and causal structure
identification (CSI) have been proposed, but are known to be
statistically inefficient and analytically intractable in high
dimensions. To overcome these limitations, we propose an
optimized ensemble approach based on support vector
regression (SVR) and dynamic Bayesian networks (DBNs). The
method called SVR-DBN, uses nonlinear kernels of the SVR to
infer the temporal relationships among genes within the DBN
framework. The two-stage ensemble is further improved by
SVR parameter optimization using Particle Swarm
Optimization. Results on eight insilico-generated datasets, and
two real world datasets of Drosophila Melanogaster and
Escherichia Coli, show that our method outperformed the
G1DBN algorithm by a total average accuracy of 12%. We
further applied our method to model the time-course
relationships of ovarian carcinoma. From our results, four hub
genes were discovered. Stratified analysis further showed that
the expression levels Prostrate differentiation factor and BTG
family member 2 genes, were significantly increased by the
cisplatin and oxaliplatin platinum drugs; while expression levels
of Polo-like kinase and Cyclin B1 genes, were both decreased by
the platinum drugs. These hub genes might be potential
biomarkers for ovarian carcinoma.
Introduction to Cytoscape talk given in March 2010 at the CRUK CRI. Cambridge UK.
It was design to give a broad introduction the features available in Cytoscape for wet lab researchers.
Outcome-guided mutual information networks for investigating gene-gene intera...SOYEON KIM
TBC2014 poster
"Outcome-guided mutual information networks for investigating gene-gene interaction effects on clinical outcomes", Hyun-hwan Jeong, So Yeon Kim, Kyubum Wee, Kyung-Ah Sohn
Uncertainty Quantification with Unsupervised Deep learning and Multi Agent Sy...Bang Xiang Yong
Presented at MET4FOF Workshop, JULY 2020
I talk about our recent work of combining Bayesian Deep learning with Explainable Artificial Intelligence (XAI) methods. In particular, we look at Bayesian Autoencoders.
Inference of Nonlinear Gene Regulatory Networks through Optimized Ensemble of...Arinze Akutekwe
Comprehensive understanding of gene regulatory
networks (GRNs) is a major challenge in systems biology. Most
methods for modeling and inferring the dynamics of GRNs,
such as those based on state space models, vector autoregressive
models and G1DBN algorithm, assume linear dependencies
among genes. However, this strong assumption does not make
for true representation of time-course relationships across the
genes, which are inherently nonlinear. Nonlinear modeling
methods such as the S-systems and causal structure
identification (CSI) have been proposed, but are known to be
statistically inefficient and analytically intractable in high
dimensions. To overcome these limitations, we propose an
optimized ensemble approach based on support vector
regression (SVR) and dynamic Bayesian networks (DBNs). The
method called SVR-DBN, uses nonlinear kernels of the SVR to
infer the temporal relationships among genes within the DBN
framework. The two-stage ensemble is further improved by
SVR parameter optimization using Particle Swarm
Optimization. Results on eight insilico-generated datasets, and
two real world datasets of Drosophila Melanogaster and
Escherichia Coli, show that our method outperformed the
G1DBN algorithm by a total average accuracy of 12%. We
further applied our method to model the time-course
relationships of ovarian carcinoma. From our results, four hub
genes were discovered. Stratified analysis further showed that
the expression levels Prostrate differentiation factor and BTG
family member 2 genes, were significantly increased by the
cisplatin and oxaliplatin platinum drugs; while expression levels
of Polo-like kinase and Cyclin B1 genes, were both decreased by
the platinum drugs. These hub genes might be potential
biomarkers for ovarian carcinoma.
Introduction to Cytoscape talk given in March 2010 at the CRUK CRI. Cambridge UK.
It was design to give a broad introduction the features available in Cytoscape for wet lab researchers.
Outcome-guided mutual information networks for investigating gene-gene intera...SOYEON KIM
TBC2014 poster
"Outcome-guided mutual information networks for investigating gene-gene interaction effects on clinical outcomes", Hyun-hwan Jeong, So Yeon Kim, Kyubum Wee, Kyung-Ah Sohn
The Bayesia Portfolio of Research SoftwareBayesia USA
The Bayesia portfolio of research software is the result of over 20 years of continuous research and development by two French professors in the field of artificial intelligence, Dr. Lionel Jouffe and Dr. Paul Munteanu. Their team of computer scientists and software developers at Bayesia S.A.S. has embraced the Bayesian networks paradigm and built tools for making it accessible to a broad audience, and practical for a wide range of research tasks.
The idea of Bayesian networks dates back to the mid-1980s, when Professor Judea Pearl of UCLA began to formalize their semantics in a series of seminal works. The study of Bayesian networks has since grown into a large body of work with dozens of books and countless scientific papers exploring all their properties.
However, thanks to Bayesia’s software tools, and the ever-increasing power of computers, Bayesian networks have become powerful and practical tool well beyond the world of academia. For applied research in all domains, Bayesian networks can facilitate deep understanding of very complex, high-dimensional problem domains. Their computational efficiency and inherently visual structure make Bayesian networks attractive for exploring and explaining complex domain. Most importantly, Bayesian networks allow reasoning about such domains in a formally correct yet highly intuitive way.
GRAPH ALGORITHM TO FIND CORE PERIPHERY STRUCTURES USING MUTUAL K-NEAREST NEIG...ijaia
Core periphery structures exist naturally in many complex networks in the real-world like social,
economic, biological and metabolic networks. Most of the existing research efforts focus on the
identification of a meso scale structure called community structure. Core periphery structures are another
equally important meso scale property in a graph that can help to gain deeper insights about the
relationships between different nodes. In this paper, we provide a definition of core periphery structures
suitable for weighted graphs. We further score and categorize these relationships into different types based
upon the density difference between the core and periphery nodes. Next, we propose an algorithm called
CP-MKNN (Core Periphery-Mutual K Nearest Neighbors) to extract core periphery structures from
weighted graphs using a heuristic node affinity measure called Mutual K-nearest neighbors (MKNN).
Using synthetic and real-world social and biological networks, we illustrate the effectiveness of developed
core periphery structures.
Graph Algorithm to Find Core Periphery Structures using Mutual K-nearest Neig...gerogepatton
Core periphery structures exist naturally in many complex networks in the real-world like social,
economic, biological and metabolic networks. Most of the existing research efforts focus on the
identification of a meso scale structure called community structure. Core periphery structures are another
equally important meso scale property in a graph that can help to gain deeper insights about the
relationships between different nodes. In this paper, we provide a definition of core periphery structures
suitable for weighted graphs. We further score and categorize these relationships into different types based
upon the density difference between the core and periphery nodes. Next, we propose an algorithm called
CP-MKNN (Core Periphery-Mutual K Nearest Neighbors) to extract core periphery structures from
weighted graphs using a heuristic node affinity measure called Mutual K-nearest neighbors (MKNN).
Using synthetic and real-world social and biological networks, we illustrate the effectiveness of developed
core periphery structures.
Graph Algorithm to Find Core Periphery Structures using Mutual K-nearest Neig...gerogepatton
Core periphery structures exist naturally in many complex networks in the real-world like social, economic, biological and metabolic networks. Most of the existing research efforts focus on the identification of a meso scale structure called community structure. Core periphery structures are another equally important meso scale property in a graph that can help to gain deeper insights about the relationships between different nodes. In this paper, we provide a definition of core periphery structures suitable for weighted graphs. We further score and categorize these relationships into different types based upon the density difference between the core and periphery nodes. Next, we propose an algorithm called CP-MKNN (Core Periphery-Mutual K Nearest Neighbors) to extract core periphery structures from weighted graphs using a heuristic node affinity measure called Mutual K-nearest neighbors (MKNN). Using synthetic and real-world social and biological networks, we illustrate the effectiveness of developed core periphery structures.
Selectivity Estimation for Hybrid Queries over Text-Rich Data GraphsWagner Andreas
Many databases today are text-rich, comprising not only structured, but also textual data. Querying such databases involves predicates matching structured data combined with string predicates featuring textual constraints. Based on selectivity estimates for these predicates, query processing as well as other tasks that can be solved through such queries can be optimized. Existing work on selectivity estimation focuses either on string or on structured query predicates alone. Further, probabilistic models proposed to incorporate dependencies between predicates are focused on the re- lational setting. In this work, we propose a template-based probabilistic model, which enables selectivity estimation for general graph-structured data. Our probabilistic model allows dependencies between structured data and its text-rich parts to be captured. With this general probabilistic solution, BN+, selectivity estimations can be obtained for queries over text-rich graph-structured data, which may contain structured and string predicates (hybrid queries). In our experiments on real-world data, we show that capturing dependencies between structured and textual data in this way greatly improves the accuracy of selectivity estimates without compromising the efficiency.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
A Study on Comparison of Bayesian Network Structure Learning Algorithms for Selecting Appropriate Models
1. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
A Study on Comparison of
Bayesian Network Structure Learning Algorithms
for Selecting Appropriate Models
Jae-seong Yoo
Dept. of Statistics
January 19, 2015
2. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Title
1 Introduction
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
2 Structure Learning Algorithms in bnlearn
Available Constraint-based Learning Algorithms
Available Score-based Learning Algorithms
Available Hybrid Learning Algorithms
3 The Comparison Methodology
The Number of Graphical Errors in the Learnt Structure
Network Scores
4 Data Generation with BN Data Generator in R
5 Simulation
Real Datasets
Synthetic Data According to Topologies
6 Discussion
3. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
Goal
In this paper, we compare the performance between the Bayesian network structure
learning algorithms provided by bnlearn package in R.
The performance is evaluated by
using a score
or
comparing between the target network and the learnt network.
In this paper, it was confirmed that algorithm specific performance test results using
fore-mentioned methods are different.
A data generator based on Bayesian network model using R is built and introduced.
The aim of this paper is to provide objective guidance of selecting suitable algorithm in
accordance to target network using synthetic data generated based on topology.
4. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
Bayesian Network
A BN defines a unique joint probability distribution over X given by
PB (X1, · · · , Xn) =
n
∏
i=1
PB (Xi | ∏
Xi
).
A BN encodes the independence assumptions over the component random variables of
X.
An edge (j, i) in E represents a direct dependency of Xi from Xj .
The set of all Bayesian networks with n variables is denotes by Bn.
Figure: P(A, B, C, D, E) = P(A)P(B|A)P(C|A)P(D|B, C)P(E|D)
5. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
기본 개념
베이지안 네트워크(이하 BN)는 확률 값이 모인 집합의 결합확률분포의 결정모델이다.
특정 분야의 영역지식을 확률적으로 표현하는 수단
변수들간의 확률적 의존 관계를 나타내는 그래프와, 각 변수별 조건부 확률로 구성된다.
하나의 BN은 각 노드마다 하나의 조건부 확률표(CPT ; Conditional Probability Table)
를 갖는 비순환유향그래프(DAG ; Directed Acycle Graph)로 정의할 수 있다.
노드와 노드를 연결하는 호(arc or edge)는 노드 사이의 관계를 나타내며, 변수의
확률적인 인과관계로 네트워크를 구성하고 조건부확률표(CPT)를 가지고 다음의 식과
같은 베이즈 정리(Bayes Theorem)을 이용하여 결과를 추론할 수 있다.
P(A|B) =
P(A, B)
P(B)
6. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
기본 개념
Figure: Nodes
7. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
기본 개념
Figure: Edges
8. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
기본 개념
Figure: Edges = directed, (No cycles!)
9. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
기본 개념
일반적인 베이지안 네트워크는 베이즈 정리, 곱셈 규칙, 체인 규칙(chain rule)에 의하여
다음과 같은 식이 만들어진다.
P(A, B, C, D, E) = ∏
i
P(xi |parenti )
여기서 x1, · · · , xn은 특정 데이터의 속성 집합
parent(xi )는 xi 의 부모 노드들의 집합
10. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
기본 개념
Figure: P(A, B, C, D, E) = ∏i P(nodei |parentsi )
11. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
기본 개념
Figure: P(A, B, C, D, E) = P(A)
12. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
기본 개념
Figure: P(A, B, C, D, E) = P(A)P(B|A)
13. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
기본 개념
Figure: P(A, B, C, D, E) = P(A)P(B|A)P(C|A)
14. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
기본 개념
Figure: P(A, B, C, D, E) = P(A)P(B|A)P(C|A)P(D|B, C)
15. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
기본 개념
Figure: P(A, B, C, D, E) = P(A)P(B|A)P(C|A)P(D|B, C)P(E|D)
16. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
S. L. Lauritzen and D. J. Spiegelhalter (1988)
S. L. Lauritzen and D. J. Spiegelhalter (1988),
”Local Computations with Probabilities on Graphical Structures
and Their Application to Expert Systems”,
Journal of the Royal Statistical Society. Series B (Methodological), Vol. 50, No. 2, pp. 157-224
Abstract
Casual Network는 변수 set에 포함되어있는 영향력의 패턴을 서술하는 것을 말하며, 많은
분야에서 사용되고 있다. 이는 전문가 시스템으로부터 크지만(large) 희소(sparse) 네트워크를
이용해 국지적 계산을 하여 구해진 평균을 통해 이 영향력을 추론하는 것이 보통이다. 이는
정확한 확률적인 방법을 이용하는 것이 불가능하였다.
이 논문에서는 original network에 위상 변화를 주어 일부 범위를 결합 확률 분포로 나타낼 수
있고, 이를 이용해서 변수 사이의 국지적 영향력을 추론할 수 있다고 설명하고 있다.
17. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
S. L. Lauritzen and D. J. Spiegelhalter (1988)
Figure: 아시아 방문 여부, 흡연 여부와 폐질환과의 관계를 도식화한 베이지안 네트워크 모형
18. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
S. L. Lauritzen and D. J. Spiegelhalter (1988)
Figure: 실제 데이터의 모습과, 이의 베이지안 네트워크 그래프 모형
19. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
S. L. Lauritzen and D. J. Spiegelhalter (1988)
Unit Test : 환자 1이 있는데, 그에 대한 정보가 아무것도 없다. P(T = 1), P(L = 1), P(B = 1)
결론 : 결핵일 가능성은 약 1%, 폐암일 가능성은 약 5%, 기관지염이 있을 가능성은 약 46%
이다.
20. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
S. L. Lauritzen and D. J. Spiegelhalter (1988)
Question 1 : 환자 2는 최근 아시아를 방문했고, 비흡연자이다. P(T = 1|A = 1, S = 0)
결론 : 기관지염이 있을 가능성이 약 34% 이다.
21. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
S. L. Lauritzen and D. J. Spiegelhalter (1988)
Question 2 : 환자 3은 최근 아시아를 방문했고 비흡연자이다. 호흡 곤란도 겪지 않고 있지만,
X-Ray 테스트 결과 양성 반응을 보였다. P(T = 1|A = 1, S = 0, D = 0, X = 1)
결론 : 환자에게 결핵과 기관지염이 있을 가능성이 각각 약 33%이다.
22. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
베이지안 네트워크의 특징
장점
특정 분야의 영역 지식을 확률적으로 표현하는 대표적인 수단
변수들 간의 확률적 의존 관계를 나타내는 그래프와 각 변수별 조건부 확률로 구성
분류 클래스 노드의 사후 확률분포를 구해줌으로써 개체들에 대한 하나의 자동분류기로
이용 가능
샘플이 어떤 부류로 분류되었을 때 왜 그런 결정이 내려졌는지 해석 가능
단점
입력값으로 수치형이 아닌 범주형을 사용
노드 수가 방대해지면 시간이 오래 소요될 수 있음
23. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
다른 기법과의 비교 - 로지스틱 회귀분석
장점
다변량 분석으로 많이 쓰임
다변량 변수를 독립변수로하여 종속변수에 미치는 영향을 파악 가능
입력값으로 수치형과 범주형 모두 취급 가능
단점
샘플이 어떤 부류로 분류되었을 때 왜 그런 결정이 내려졌는지 해석하기가 어려움
분석자료에 가장 적합한 모델을 선정하는 데 시간 투자가 필요
24. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
다른 기법과의 비교 - 신경망
장점
분류문제 뿐만 아니라 예측, 평가, 합성, 제어 등의 다양한 분야에 적용 가능
학습능력을 갖추고 일반화 능력이 뛰어나고 구현이 쉬움
다층 퍼셉트론은 선형분리가 불가능한 경우에도 높은 성능을 보여주는 한 단계 진보한
신경망
단점
샘플이 어떤 부류로 분류되었을 때 왜 그런 결정이 내려졌는지 이유를 분석하기가
어려움
입력값으로 수치형이 아닌 범주형을 사용
25. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
다른 기법과의 비교 - 의사 결정 트리
장점
의사결정규칙을 도표화하여 관심대상에 해당하는 집단을 몇 개의 소집단으로
분류하거나 예측을 수행하는 계량적 분석 방법
샘플이 어떤 부류로 분류되었을 때 왜 그런 결정이 내려졌는지 해석 가능
입력값으로 수치형, 범주형 모두 취급 가능
단점
반응변수가 수치형인 회귀모형에서는 그 예측력이 떨어짐
나무가 너무 깊은 경우에는 예측력 저하와 해석이 쉽지 않음
가지가 많을 경우 새로운 자료에 적용할 때 예측 오차가 큼
26. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
베이지안 네트워크의 다양한 유형
나이브 베이지안 네트워크 (NBN)
가정의 단순함에도 불구하고 많은 연구를 통해 비교적 높은 분류성능을 보여준다.
일반 베이지안 네트워크 (GBN)
클래스 노드조차 일반 속성 노드와의 차이를 두지 않고 모든 노드들 간의 상호의존도를
하나의 베이지안 네트워크로 표현한다.
트리-확장 나이브 베이지안 네트워크 (TAN)
속성 노드들 간에도 상호의존도가 존재한다고 가정하고, 이러한 속성 간 상호의존도를
하나의 일반 베이지안 네트워크 형태로 표현 가능하도록 NBN을 확장한 것이다.
동적 베이지안 네트워크 (DBN)
시계열 분석을 위해 현재 변수의 확률을 계산할 때, 이전 시점의 정보를 함께 고려하는
베이지안 네트워크이다.
27. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
베이지안 네트워크의 다양한 유형
28. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
베이지안 네트워크의 다양한 유형
29. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
Bayesian Network Structure Learning
Learning a Bayesian network is as follows:
Given a data T = {y1, · · · , yn} and a scoring function φ, the problem of learning a Bayesian
network is to find a Bayesian network B ∈ Bn that maximizes the value φ(B, T).
Figure: A model before learning structure
30. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Available Constraint-based Learning Algorithms
Available Score-based Learning Algorithms
Available Hybrid Learning Algorithms
Available constraint-based learning algorithms
Grow-Shrink (GS) based on the Grow-Shrink Markov Blanket, the first (and simplest) Markov
blanket detection algorithm used in a structure learning algorithm.
Incremental Association (IAMB) based on the Markov blanket detection algorithm of the
same name, which is based on a two-phase selection scheme (a forward
selection followed by an attempt to remove false positives).
31. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Available Constraint-based Learning Algorithms
Available Score-based Learning Algorithms
Available Hybrid Learning Algorithms
Available Score-based Learning Algorithms
Hill-Climbing (HC) a hill climbing greedy search on the space of the directed graphs. The
optimized implementation uses score caching, score decomposability and
score equivalence to reduce the number of duplicated tests.
Tabu Search (TABU) a modified hill climbing able to escape local optima by selecting a
network that minimally decreases the score function.
32. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Available Constraint-based Learning Algorithms
Available Score-based Learning Algorithms
Available Hybrid Learning Algorithms
Available Hybrid Learning Algorithms
Max-Min Hill-Climbing (MHHC) a hybrid algorithm which combines the Max-Min Parents
and Children algorithm (to restrict the search space) and the Hill-Climbing
algorithm (to find the optimal network structure in the restricted space).
Restricted Maximization (RSMAX2) a more general implementation of the Max-Min
Hill-Climbing, which can use any combination of constraint-based and
score-based algorithms.
33. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
The Number of Graphical Errors in the Learnt Structure
Network Scores
The Number of Graphical Errors in the Learnt
Structure
In terms of the number of graphical errors in the learnt structure.
Target Network Learnt Network Direction
C (Correct Arcs) exist exist correct
M (Missing Arcs) exist not exist
WO (Wrongly Oriented Arcs) exist exist wrong
WC (Wrongly Corrected Arcs) not exist exist
34. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
The Number of Graphical Errors in the Learnt Structure
Network Scores
Network Scores
In all four cases, the higher the value of the metric, the better the network.
BDe BDe(B, T) = P(B, T) = P(B) × ∏n
i=1 ∏
qi
j=1(
Γ(Nij )
Γ(Nij +Nij )
× ∏
ri
k=1
Γ(Nijk +Nijk )
Γ(Nijk )
)
φ(B|T) = LL(B|T) − f (N)|B|,
Log-Likelihood(LL) If f (N) = 0, we have the LL score.
AIC If f(N) = 1, we have the AIC scoring function:
BIC If f (N) = 1
2 log(N), we have the BIC score.
35. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Data Generation with BN Data Generator in R
BN Data Generator {BNDataGenerator}
Description It based on a Bayesian network model to generates synthetic data.
Usage BN Data Generator (arcs, Probs, n, node names)
Suppose bnlearn
Repository CRAN (Submitted at 2014-12-28)
URL https://github.com/praster1/BN Data Generator
Arguments
arcs (matrix) A matrix that determines the arcs.
Probs (list) The conditional probabilities.
n (constant) Data Size
node names (vector) Node names
36. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Data Generation with BN Data Generator in R
37. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Data Generation with BN Data Generator in R
38. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Outline
1 Introduction
Goal
Bayesian Network
베이지안 네트워크의 특징
다른 기법과의 장단점 비교
베이지안 네트워크의 다양한 유형
Bayesian Network Structure Learning
2 Structure Learning Algorithms in bnlearn
Available Constraint-based Learning Algorithms
Available Score-based Learning Algorithms
Available Hybrid Learning Algorithms
3 The Comparison Methodology
The Number of Graphical Errors in the Learnt Structure
Network Scores
4 Data Generation with BN Data Generator in R
5 Simulation
Real Datasets
Synthetic Data According to Topologies
6 Discussion
39. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Asia DataSet
Description Small synthetic data set from Lauritzen and Spiegelhalter (1988) about lung
diseases (tuberculosis, lung cancer or bronchitis) and visits to Asia.
Number of nodes 8
Number of arcs 8
Number of parameters 18
Source Lauritzen S, Spiegelhalter D (1988).
”Local Computation with Probabilities on Graphical Structures and their
Application to Expert Systems (with discussion)”.
Journal of the Royal Statistical Society: Series B (Statistical Methodology),
50(2), 157-224.
40. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Asia DataSet
41. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Insurance DataSet
Description Insurance is a network for evaluating car insurance risks.
Number of nodes 27
Number of arcs 52
Number of parameters 984
Source Binder J, Koller D, Russell S, Kanazawa K (1997).
”Adaptive Probabilistic Networks with Hidden Variables”.
Machine Learning, 29(2-3), 213-244.
42. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Insurance DataSet
43. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Alarm DataSet
Description The ALARM (”A Logical Alarm Reduction Mechanism”) is a Bayesian
network designed to provide an alarm message system for patient monitoring.
Number of nodes 37
Number of arcs 46
Number of parameters 509
Source Beinlich I, Suermondt HJ, Chavez RM, Cooper GF (1989).
”The ALARM Monitoring System: A Case Study with Two Probabilistic
Inference Techniques for Belief Networks.”
In ”Proceedings of the 2nd European Conference on Artificial Intelligence in
Medicine”, pp. 247-256. Springer-Verlag.
44. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Alarm DataSet
45. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
HailFinder DataSet
Description Hailfinder is a Bayesian network designed to forecast severe summer hail in
northeastern Colorado.
Number of nodes 56
Number of arcs 66
Number of parameters 2656
Source Abramson B, Brown J, Edwards W, Murphy A, Winkler RL (1996).
”Hailfinder: A Bayesian system for forecasting severe weather”.
International Journal of Forecasting, 12(1), 57-71.
46. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
HailFinder DataSet
47. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Summary
48. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Varying topologies and number of nodes
Eitel J. M. Laur´ıa,
”An Information-Geometric Approach to Learning Bayesian Network Topologies from Data”,
Innovations in Bayesian Networks Studies in Computational Intelligence Volume 156, 2008, pp 187-217
49. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Prerequisite
Cardinality was limited to two.
The probability value, which is imparted optionally under U(0, 1) distribution.
All experiments are repeated 100 times, and overall results are reported.
Constraint-based Learning Algorithms often makes undirected arcs. So, this has been
excluded from comparison.
50. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Collapse
51. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Collapse (Score)
52. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Collapse (Arcs)
53. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Collapse
54. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Line
55. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Line (Score)
56. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Line (Arcs)
57. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Line
58. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Star
59. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Star (Score)
60. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Star (Arcs)
61. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Star
62. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
PseudoLoop
63. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
PseudoLoop (Score)
64. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
PseudoLoop (Arc)
65. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
PseudoLoop
66. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Diamond
67. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Diamond (Score)
68. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Diamond (Arc)
69. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Diamond
70. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Rhombus
71. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Rhombus (Score)
72. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Rhombus (Arc)
73. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Real Datasets
Synthetic Data According to Topologies
Rhombus
74. Introduction
Structure Learning Algorithms in bnlearn
The Comparison Methodology
Data Generation with BN Data Generator in R
Simulation
Discussion
Discussion
In most cases using synthetic data according to topology,
If comparing by score, then TABU search has good performance.
But comparing by reference to ”What C is the lot?”, then HC has also good
performance.
Hybrid algorithm compared to Score-based algorithm is found to be that draw the arc
more conservative.
About Line and Star form, the performance difference due to relatively algorithm was
not large compared to other topology.