Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks

نویسندگان

  • Tom Heskes
  • Onno Zoeter
چکیده

We describe expectation propagation for ap­ proximate inference in dynamic Bayesian net­ works as a natural extension of Pearl's ex­ act belief propagation. Expectation propa­ gation is a greedy algorithm, converges in many practical cases, but not always. We de­ rive a double-loop algorithm, guaranteed to converge to a local minimum of a Bethe free energy. Furthermore, we show that stable fixed points of (damped) expectation prop­ agation correspond to local minima of this free energy, but that the converse need not be the case. We illustrate the algorithms by applying them to switching linear dynamical systems and discuss implications for approxi­ mate inference in general Bayesian networks.

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

An Introduction to Inference and Learning in Bayesian Networks

Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...

متن کامل

On the Concentration of Expectation and Approximate Inference in Layered Networks

We present an analysis of concentration-of-expectation phenomena in layered Bayesian networks that use generalized linear models as the local conditional probabilities. This framework encompasses a wide variety of probability distributions, including both discrete and continuous random variables. We utilize ideas from large deviation analysis and the delta method to devise and evaluate a class ...

متن کامل

Visualization of process data with dynamic Bayesian networks

We describe a novel visualization algorithm for high-dimensional timeseries data. The underlying model is a switching linear dynamical system, a particular variant of a dynamic Bayesian network. An important difference with most existing visualization techniques is the possibility to incorporate time dependencies between data points. Exact inference in switching linear dynamical systems is intr...

متن کامل

Importance Sampling for Continuous Time Bayesian Networks

A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact inference in a CTBN is often intractable as the state space of the dynamic system grows exponentially with the number of variables. In this paper, we first present an approximate inference algorithm based on importance sam...

متن کامل

Expectation Propagation for Continuous Time Bayesian Networks

Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. As shown previously, exact inference in CTBNs is ...

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:

دوره   شماره 

صفحات  -

تاریخ انتشار 2002