Approximation algorithms for restricted Bayesian network structures

نویسنده

  • Valentin Ziegler
چکیده

Bayesian Network structures with a maximum in-degree of k can be approximated with respect to a positive scoring metric up to an factor of 1/k.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

The modeling of body's immune system using Bayesian Networks

In this paper, the urinary infection, that is a common symptom of the decline of the immune system, is discussed based on the well-known algorithms in machine learning, such as Bayesian networks in both Markov and tree structures. A large scale sampling has been executed to evaluate the performance of Bayesian network algorithm. A number of 4052 samples wereobtained from the database of the Tak...

متن کامل

Improve Estimation and Operation of Optimal Power Flow(OPF) Using Bayesian Neural Network

The future of development and design is impossible without study of Power Flow(PF), exigency the system outcomes load growth, necessity add generators, transformers and power lines in  power system. The urgency for Optimal Power Flow (OPF) studies, in addition to the items listed for the PF and in order to achieve the objective functions. In this paper has been used cost of generator fuel, acti...

متن کامل

Efficient Approximation Algorithms for Point-set Diameter in Higher Dimensions

We study the problem of computing the diameter of a  set of $n$ points in $d$-dimensional Euclidean space for a fixed dimension $d$, and propose a new $(1+varepsilon)$-approximation algorithm with $O(n+ 1/varepsilon^{d-1})$ time and $O(n)$ space, where $0 < varepsilonleqslant 1$. We also show that the proposed algorithm can be modified to a $(1+O(varepsilon))$-approximation algorithm with $O(n+...

متن کامل

A Depth-First Branch and Bound Algorithm for Learning Optimal Bayesian Networks

Early methods for learning a Bayesian network that optimizes a scoring function for a given dataset are mostly approximation algorithms such as greedy hill climbing approaches. These methods are anytime algorithms as they can be stopped anytime to produce the best solution so far. However, they cannot guarantee the quality of their solution, not even mentioning optimality. In recent years, seve...

متن کامل

Lower Bound Bayesian Networks - An Efficient Inference of Lower Bounds on Probability Distributions in Bayesian Networks

We present a new method to propagate lower bounds on conditional probability distributions in conventional Bayesian networks. Our method guarantees to provide outer approximations of the exact lower bounds. A key advantage is that we can use any available algorithms and tools for Bayesian networks in order to represent and infer lower bounds. This new method yields results that are provable exa...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Inf. Process. Lett.

دوره 108  شماره 

صفحات  -

تاریخ انتشار 2008