Главная    Ex Libris    Книги    Журналы    Статьи    Серии    Каталог    Wanted    Загрузка    ХудЛит    Справка    Поиск по индексам    Поиск    Форум   
blank
Авторизация

       
blank
Поиск по указателям

blank
blank
blank
Красота
blank
Kjaerulff U., Madsen A. — Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis (Information Science and Statistics)
Kjaerulff U., Madsen A. — Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis (Information Science and Statistics)



Обсудите книгу на научном форуме



Нашли опечатку?
Выделите ее мышкой и нажмите Ctrl+Enter


Название: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis (Information Science and Statistics)

Авторы: Kjaerulff U., Madsen A.

Аннотация:

This book is a monograph on practical aspects of probabilistic networks (a.k.a.
probabilistic graphical models) and is intended to provide a comprehensive
guide for practitioners that wish to understand, construct, and analyze decision
support systems based on probabilistic networks, including a number
of different variants of Bayesian networks and influence diagrams. The book
consists of three parts:
• Part I: Fundamentals of probabilistic networks, including Chapters 1–5,
covering a brief introduction to probabilistic graphical models, the basic
graph-theoretic terminology, the basic (Bayesian) probability theory, the
key concepts of (conditional) dependence and independence, the different
varieties of probabilistic networks, and methods for making inference in
these kinds of models. This part can be skipped by readers with fundamental
knowledge about probabilistic networks.
• Part II: Model construction, including Chapters 6–8, covering methods and
techniques for elicitation of model structure and parameters, a large number
of useful techniques and tricks to solve commonly recurring modeling
problems, and methods for constructing probabilistic networks automatically
from data, possibly through fusion of data and expert knowledge.
Chapters 6 and 7 offer concrete advice and techniques on issues related
to model construction, and Chapter 8 explains the theory and methods
behind learning of Bayesian networks from data.
• Part III: Model analysis, including Chapters 9–11, covering conflict analysis
for detecting conflicting pieces of evidence (observations) or evidence
that conflicts with the model, sensitivity analysis of a model both with
respect to variations of evidence and model parameters, and value of information
analysis. This part explains the theory and methods underlying
the three different kinds of analyses.


Язык: en

Рубрика: Разное/

Статус предметного указателя: Неизвестно

ed2k: ed2k stats

Год издания: 2008

Количество страниц: 336

Добавлена в каталог: 08.04.2018

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
blank
Предметный указатель
blank
Реклама
blank
blank
HR
@Mail.ru
       © Электронная библиотека попечительского совета мехмата МГУ, 2004-2021
Электронная библиотека мехмата МГУ | Valid HTML 4.01! | Valid CSS! О проекте