• Corpus ID: 19757051

Hybrid Bayesian networks for reasoning about complex systems

@inproceedings{Lerner2002HybridBN,
  title={Hybrid Bayesian networks for reasoning about complex systems},
  author={Uri Lerner},
  year={2002}
}
Many real-world systems are naturally modeled as hybrid stochastic processes, i.e., stochastic processes that contain both discrete and continuous variables. Examples include speech recognition, target tracking, and monitoring of physical systems. The task is usually to perform probabilistic inference, i.e., infer the hidden state of the system given some noisy observations. For example, we can ask what is the probability that a certain word was pronounced given the readings of our microphone… 

Efficient Inference For Hybrid Bayesian Networks

This dissertation focuses on the hybrid Bayesian networks containing both discrete and continuous random variables and presents an approximate analytical method to estimate the performance bound, which can help the decision maker to understand the prediction performance of a BN model without extensive simulation.

Performance modeling for dynamic Bayesian networks

Comparison and analysis of the experimental results show the potential capability of the sequential simulation method based on the particle filter concept for evaluating the performance of dynamic Bayesian networks.

Almost instant time inference for hybrid partially dynamic Bayesian networks

  • K. Chang
  • Computer Science
    IEEE Transactions on Aerospace and Electronic Systems
  • 2007
The main contribution here is a unified treatment of arbitrary (nonlinear non-Gaussian) hybrid (discrete and continuous) BN inference having both computation and accuracy scalability.

A generalized hybrid fuzzy-bayesian methodology for modeling complex uncertainty

A novel framework to implement a hybrid methodology that complements probability theory with Fuzzy Sets to perform exact inferencing with general Hybrid Bayesian Networks that is composed of both discrete and continuous variables with no graph-structural restrictions to model uncertainty in complex systems is provided.

Cubature Kalman Filtering Theory & Applications

The challenge ahead of us is to derive an approximate nonlinear Bayesian filter, which is theoretically motivated, reasonably accurate, and easily extendable to a wide range of applications at a minimal computational cost.

State estimation of probabilistic hybrid systems with particle filters

An efficient algorithm for hybrid state estimation that combines Rao-Blackwellised particle filtering with a Gaussian representation is introduced and lays ground work for a unifying stochastic search algorithm that shares the benefits of both methods.

Inference in hybrid Bayesian networks using mixtures of polynomials

Gaussian Particle Filtering for Concurrent Hybrid Models Gaussian Particle Filtering for Concurrent Hybrid Models with Autonomous Transitions

An efficient algorithm for hybrid state estimation that combines Rao-Blackwellised particle filtering with a Gaussian representation is introduced and lays ground work for a unifying stochastic search algorithm that shares the benefits of both methods.

Comparing probabilistic inference for mixed Bayesian networks

This paper compares and analyze the trade-offs for several inference approaches, including the exact Junction Tree algorithm for linear Gaussian networks, the exact algorithm for discretized networks, and the stochastic simulation methods, and proposes an almost instant-time algorithm (AIA) by pre-compiling the approximate likelihood tables.

Extended Shenoy-Shafer architecture for inference in hybrid bayesian networks with deterministic conditionals

...

References

SHOWING 1-5 OF 5 REFERENCES

A Course in Probability and Statistics

1. RANDOM VARIABLES AND THEIR DISTRIBUTION Introduction / Sample Distributions / Distributions / Random Variables / Probability Functions and Density Functions / Distribution Functions and Quantiles

Graphical models in R

This paper presents Graphical Models for Complex Stochastic Systems, a meta-modelling framework for graphical models of complex systems that combines Gaussian Graphical models, Mixed Interaction Models, and Log-Linear Models.

Elements of Information Theory

The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.

Introduction to Numerical Analysis

1. The numerical evaluation of expressions 2. Linear systems of equations 3. Interpolation and numerical differentiation 4. Numerical integration 5. Univariate nonlinear equations 6. Systems of

Vandenberghe

  • Convex Optimization.
  • 2003