17,918 research outputs found
Stochastic Simulation of Bayesian Belief Networks
This paper examines Bayesian belief network inference using simulation as a
method for computing the posterior probabilities of network variables.
Specifically, it examines the use of a method described by Henrion, called
logic sampling, and a method described by Pearl, called stochastic simulation.
We first review the conditions under which logic sampling is computationally
infeasible. Such cases motivated the development of the Pearl's stochastic
simulation algorithm. We have found that this stochastic simulation algorithm,
when applied to certain networks, leads to much slower than expected
convergence to the true posterior probabilities. This behavior is a result of
the tendency for local areas in the network to become fixed through many
simulation cycles. The time required to obtain significant convergence can be
made arbitrarily long by strengthening the probabilistic dependency between
nodes. We propose the use of several forms of graph modification, such as graph
pruning, arc reversal, and node reduction, in order to convert some networks
into formats that are computationally more efficient for simulation.Comment: Appears in Proceedings of the Third Conference on Uncertainty in
Artificial Intelligence (UAI1987
Spintronics based Stochastic Computing for Efficient Bayesian Inference System
Bayesian inference is an effective approach for solving statistical learning
problems especially with uncertainty and incompleteness. However, inference
efficiencies are physically limited by the bottlenecks of conventional
computing platforms. In this paper, an emerging Bayesian inference system is
proposed by exploiting spintronics based stochastic computing. A stochastic
bitstream generator is realized as the kernel components by leveraging the
inherent randomness of spintronics devices. The proposed system is evaluated by
typical applications of data fusion and Bayesian belief networks. Simulation
results indicate that the proposed approach could achieve significant
improvement on inference efficiencies in terms of power consumption and
inference speed.Comment: accepted by ASPDAC 2018 conferenc
An Empirical Evaluation of a Randomized Algorithm for Probabilistic Inference
In recent years, researchers in decision analysis and artificial intelligence
(Al) have used Bayesian belief networks to build models of expert opinion.
Using standard methods drawn from the theory of computational complexity,
workers in the field have shown that the problem of probabilistic inference in
belief networks is difficult and almost certainly intractable. K N ET, a
software environment for constructing knowledge-based systems within the
axiomatic framework of decision theory, contains a randomized approximation
scheme for probabilistic inference. The algorithm can, in many circumstances,
perform efficient approximate inference in large and richly interconnected
models of medical diagnosis. Unlike previously described stochastic algorithms
for probabilistic inference, the randomized approximation scheme computes a
priori bounds on running time by analyzing the structure and contents of the
belief network. In this article, we describe a randomized algorithm for
probabilistic inference and analyze its performance mathematically. Then, we
devote the major portion of the paper to a discussion of the algorithm's
empirical behavior. The results indicate that the generation of good trials
(that is, trials whose distribution closely matches the true distribution),
rather than the computation of numerous mediocre trials, dominates the
performance of stochastic simulation. Key words: probabilistic inference,
belief networks, stochastic simulation, computational complexity theory,
randomized algorithms.Comment: Appears in Proceedings of the Fifth Conference on Uncertainty in
Artificial Intelligence (UAI1989
Backward Simulation in Bayesian Networks
Backward simulation is an approximate inference technique for Bayesian belief
networks. It differs from existing simulation methods in that it starts
simulation from the known evidence and works backward (i.e., contrary to the
direction of the arcs). The technique's focus on the evidence leads to improved
convergence in situations where the posterior beliefs are dominated by the
evidence rather than by the prior probabilities. Since this class of situations
is large, the technique may make practical the application of approximate
inference in Bayesian belief networks to many real-world problems.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
An Importance Sampling Algorithm Based on Evidence Pre-propagation
Precision achieved by stochastic sampling algorithms for Bayesian networks
typically deteriorates in face of extremely unlikely evidence. To address this
problem, we propose the Evidence Pre-propagation Importance Sampling algorithm
(EPIS-BN), an importance sampling algorithm that computes an approximate
importance function by the heuristic methods: loopy belief Propagation and
e-cutoff. We tested the performance of e-cutoff on three large real Bayesian
networks: ANDES, CPCS, and PATHFINDER. We observed that on each of these
networks the EPIS-BN algorithm gives us a considerable improvement over the
current state of the art algorithm, the AIS-BN algorithm. In addition, it
avoids the costly learning stage of the AIS-BN algorithm.Comment: Appears in Proceedings of the Nineteenth Conference on Uncertainty in
Artificial Intelligence (UAI2003
Planning and Acting under Uncertainty: A New Model for Spoken Dialogue Systems
Uncertainty plays a central role in spoken dialogue systems. Some stochastic
models like Markov decision process (MDP) are used to model the dialogue
manager. But the partially observable system state and user intention hinder
the natural representation of the dialogue state. MDP-based system degrades
fast when uncertainty about a user's intention increases. We propose a novel
dialogue model based on the partially observable Markov decision process
(POMDP). We use hidden system states and user intentions as the state set,
parser results and low-level information as the observation set, domain actions
and dialogue repair actions as the action set. Here the low-level information
is extracted from different input modals, including speech, keyboard, mouse,
etc., using Bayesian networks. Because of the limitation of the exact
algorithms, we focus on heuristic approximation algorithms and their
applicability in POMDP for dialogue management. We also propose two methods for
grid point selection in grid-based approximation algorithms.Comment: Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001
Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics
In order to interact intelligently with objects in the world, animals must
first transform neural population responses into estimates of the dynamic,
unknown stimuli which caused them. The Bayesian solution to this problem is
known as a Bayes filter, which applies Bayes' rule to combine population
responses with the predictions of an internal model. In this paper we present a
method for learning to approximate a Bayes filter when the stimulus dynamics
are unknown. To do this we use the inferential properties of probabilistic
population codes to compute Bayes' rule, and train a neural network to compute
approximate predictions by the method of maximum likelihood. In particular, we
perform stochastic gradient descent on the negative log-likelihood with a novel
approximation of the gradient. We demonstrate our methods on a finite-state, a
linear, and a nonlinear filtering problem, and show how the hidden layer of the
neural network develops tuning curves which are consistent with findings in
experimental neuroscience.Comment: This is the final version, and has been accepted for publication in
Neural Computatio
Hardware implementation of Bayesian network building blocks with stochastic spintronic devices
Bayesian networks are powerful statistical models to understand causal
relationships in real-world probabilistic problems such as diagnosis,
forecasting, computer vision, etc. For systems that involve complex causal
dependencies among many variables, the complexity of the associated Bayesian
networks become computationally intractable. As a result, direct hardware
implementation of these networks is one promising approach to reducing power
consumption and execution time. However, the few hardware implementations of
Bayesian networks presented in literature rely on deterministic CMOS devices
that are not efficient in representing the inherently stochastic variables in a
Bayesian network. This work presents an experimental demonstration of a
Bayesian network building block implemented with naturally stochastic
spintronic devices. These devices are based on nanomagnets with perpendicular
magnetic anisotropy, initialized to their hard axes by the spin orbit torque
from a heavy metal under-layer utilizing the giant spin Hall effect, enabling
stochastic behavior. We construct an electrically interconnected network of two
stochastic devices and manipulate the correlations between their states by
changing connection weights and biases. By mapping given conditional
probability tables to the circuit hardware, we demonstrate that any two node
Bayesian networks can be implemented by our stochastic network. We then present
the stochastic simulation of an example case of a four node Bayesian network
using our proposed device, with parameters taken from the experiment. We view
this work as a first step towards the large scale hardware implementation of
Bayesian networks.Comment: 9 pages, 4 figure
Importance Sampling via Variational Optimization
Computing the exact likelihood of data in large Bayesian networks consisting
of thousands of vertices is often a difficult task. When these models contain
many deterministic conditional probability tables and when the observed values
are extremely unlikely even alternative algorithms such as variational methods
and stochastic sampling often perform poorly. We present a new importance
sampling algorithm for Bayesian networks which is based on variational
techniques. We use the updates of the importance function to predict whether
the stochastic sampling converged above or below the true likelihood, and
change the proposal distribution accordingly. The validity of the method and
its contribution to convergence is demonstrated on hard networks of large
genetic linkage analysis tasks.Comment: Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007
QMDP-Net: Deep Learning for Planning under Partial Observability
This paper introduces the QMDP-net, a neural network architecture for
planning under partial observability. The QMDP-net combines the strengths of
model-free learning and model-based planning. It is a recurrent policy network,
but it represents a policy for a parameterized set of tasks by connecting a
model with a planning algorithm that solves the model, thus embedding the
solution structure of planning in a network learning architecture. The QMDP-net
is fully differentiable and allows for end-to-end training. We train a QMDP-net
on different tasks so that it can generalize to new ones in the parameterized
task set and "transfer" to other similar tasks beyond the set. In preliminary
experiments, QMDP-net showed strong performance on several robotic tasks in
simulation. Interestingly, while QMDP-net encodes the QMDP algorithm, it
sometimes outperforms the QMDP algorithm in the experiments, as a result of
end-to-end learning.Comment: NIPS 2017 camera-read
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