Approxamation methods for efficient learning of Bayesian networks 1st Edition by Carsten Riggelsen ISBN 1586038214 9781586038212

Approxamation methods for efficient learning of Bayesian networks 1st Edition by Carsten Riggelsen – Ebook PDF Instant Download/Delivery. 1586038214 ,9781586038212
Full download Approxamation methods for efficient learning of Bayesian networks 1st Edition after payment

Product details:
ISBN 10: 1586038214
ISBN 13: 9781586038212
Author: Carsten Riggelsen
This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. Topics discussed are; basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

IOS Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields.

Some of the areas we publish in:

-Biomedicine
-Oncology
-Artificial intelligence
-Databases and information systems
-Maritime engineering
-Nanotechnology
-Geoengineering
-All aspects of physics
-E-governance
-E-commerce
-The knowledge economy
-Urban studies
-Arms control
-Understanding and responding to terrorism
-Medical informatics
-Computer Sciences

Approxamation methods for efficient learning of Bayesian networks 1st Edition Table of contents:

Part I: Foundations of Bayesian Networks

2. Fundamentals of Probability and Graphical Models

   • Basic Probability Theory
   • Conditional Probability and Independence
   • Introduction to Graphical Models
   • Directed Acyclic Graphs (DAGs) and their Properties
   • Types of Bayesian Networks: Static vs. Dynamic

3. Learning Bayesian Networks: Challenges and Algorithms

   • Structure Learning vs. Parameter Learning
   • Exact vs. Approximate Learning Methods
   • Algorithms for Structure Learning: Score-Based, Constraint-Based, and Hybrid Approaches
   • Algorithms for Parameter Learning: Maximum Likelihood Estimation, Bayesian Estimation
   • Identifiability and Overfitting in Bayesian Networks

---

Part II: Approximation Methods

4. Approximate Inference Methods

   • Importance of Approximation in Large-Scale Bayesian Networks
   • Approximate Inference Algorithms: MCMC, Variational Inference, and Loopy Belief Propagation
   • Sampling Techniques: Gibbs Sampling, Importance Sampling, and Metropolis-Hastings
   • Variational Methods and Expectation Propagation
   • Comparison of Approximate Inference Methods

5. Approximate Structure Learning

   • Challenges in Structure Learning for Large Networks
   • Greedy Search Algorithms and their Limitations
   • Heuristic and Monte Carlo Methods for Structure Learning
   • The Role of Approximation in Reducing Computational Complexity
   • Approximate Search Methods: Genetic Algorithms, Simulated Annealing
   • Case Studies in Approximate Structure Learning

6. Parallel and Distributed Approaches for Efficient Learning

   • Parallelization Techniques for Learning Large Bayesian Networks
   • Distributed Computing Models for Bayesian Network Learning
   • The Role of Cloud Computing and Big Data in Bayesian Network Learning
   • Trade-Offs Between Accuracy and Computational Efficiency
   • Scalability of Approximation Methods in Large Datasets

---

Part III: Advanced Topics and Applications

7. Hierarchical and Dynamic Bayesian Networks

   • Introduction to Hierarchical Bayesian Networks
   • Dynamic Bayesian Networks for Time-Series Data
   • Approximation Techniques for Dynamic Bayesian Networks
   • Inference in Hierarchical Models
   • Learning Complex Temporal Dependencies in Bayesian Networks

8. Evaluation of Approximation Methods

   • Metrics for Evaluating Approximation Accuracy
   • Empirical Validation of Approximation Techniques
   • Trade-Offs Between Precision and Efficiency
   • Cross-Validation in Bayesian Network Learning
   • Case Studies on Evaluation of Approximate Learning Methods

9. Real-World Applications and Case Studies

   • Applications of Bayesian Networks in Medicine and Healthcare
   • Learning Bayesian Networks for Natural Language Processing
   • Bayesian Networks in Robotics and Autonomous Systems
   • Case Study: Using Approximate Methods in Large-Scale Data Systems
   • Practical Challenges and Solutions in Real-World Applications

---

Conclusion
10. Conclusion and Future Directions

• Summary of Key Findings and Approaches
• The Future of Approximation Methods in Bayesian Network Learning
• Open Research Problems and Challenges
• The Role of Deep Learning and Bayesian Networks
• Final Thoughts on the Future of Efficient Bayesian Network Learning

People also search for Approxamation methods for efficient learning of Bayesian networks 1st Edition:

approximation methods for bilevel programming

efficient methods for natural language processing a survey

approximation method

an efficient approach for assessing hyperparameter importance