6,251 research outputs found
Expectation Maximization and Complex Duration Distributions 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. We address the problem of learning the
parameters and structure of a CTBN from partially observed data. We show how to
apply expectation maximization (EM) and structural expectation maximization
(SEM) to CTBNs. The availability of the EM algorithm allows us to extend the
representation of CTBNs to allow a much richer class of transition durations
distributions, known as phase distributions. This class is a highly expressive
semi-parametric representation, which can approximate any duration distribution
arbitrarily closely. This extension to the CTBN framework addresses one of the
main limitations of both CTBNs and DBNs - the restriction to exponentially /
geometrically distributed duration. We present experimental results on a real
data set of people's life spans, showing that our algorithm learns reasonable
models - structure and parameters - from partially observed data, and, with the
use of phase distributions, achieves better performance than DBNs.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005
Inference from Randomized Transmissions by Many Backscatter Sensors
Attaining the vision of Smart Cities requires the deployment of an enormous
number of sensors for monitoring various conditions of the environment.
Backscatter-sensors have emerged to be a promising solution due to the
uninterruptible energy supply and relative simple hardwares. On the other hand,
backscatter-sensors with limited signal-processing capabilities are unable to
support conventional algorithms for multiple-access and channel-training. Thus,
the key challenge in designing backscatter-sensor networks is to enable readers
to accurately detect sensing-values given simple ALOHA random access, primitive
transmission schemes, and no knowledge of channel-states. We tackle this
challenge by proposing the novel framework of backscatter sensing featuring
random-encoding at sensors and statistical-inference at readers. Specifically,
assuming the on/off keying for backscatter transmissions, the practical
random-encoding scheme causes the on/off transmission of a sensor to follow a
distribution parameterized by the sensing values. Facilitated by the scheme,
statistical-inference algorithms are designed to enable a reader to infer
sensing-values from randomized transmissions by multiple sensors. The specific
design procedure involves the construction of Bayesian networks, namely
deriving conditional distributions for relating unknown parameters and
variables to signals observed by the reader. Then based on the Bayesian
networks and the well-known expectation-maximization principle, inference
algorithms are derived to recover sensing-values.Comment: An extended version of a shorter conference submissio
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 intractable. We address the problem of approximate inference, allowing
for general queries conditioned on evidence over continuous time intervals and
at discrete time points. We show how CTBNs can be parameterized within the
exponential family, and use that insight to develop a message passing scheme in
cluster graphs and allows us to apply expectation propagation to CTBNs. The
clusters in our cluster graph do not contain distributions over the cluster
variables at individual time points, but distributions over trajectories of the
variables throughout a duration. Thus, unlike discrete time temporal models
such as dynamic Bayesian networks, we can adapt the time granularity at which
we reason for different variables and in different conditions.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005
Learning Continuous-Time Social Network Dynamics
We demonstrate that a number of sociology models for social network dynamics
can be viewed as continuous time Bayesian networks (CTBNs). A sampling-based
approximate inference method for CTBNs can be used as the basis of an
expectation-maximization procedure that achieves better accuracy in estimating
the parameters of the model than the standard method of moments
algorithmfromthe sociology literature. We extend the existing social network
models to allow for indirect and asynchronous observations of the links. A
Markov chain Monte Carlo sampling algorithm for this new model permits
estimation and inference. We provide results on both a synthetic network (for
verification) and real social network data.Comment: Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009
Factored Filtering of Continuous-Time Systems
We consider filtering for a continuous-time, or asynchronous, stochastic
system where the full distribution over states is too large to be stored or
calculated. We assume that the rate matrix of the system can be compactly
represented and that the belief distribution is to be approximated as a product
of marginals. The essential computation is the matrix exponential. We look at
two different methods for its computation: ODE integration and uniformization
of the Taylor expansion. For both we consider approximations in which only a
factored belief state is maintained. For factored uniformization we demonstrate
that the KL-divergence of the filtering is bounded. Our experimental results
confirm our factored uniformization performs better than previously suggested
uniformization methods and the mean field algorithm
Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL
Humans and animals can learn complex predictive models that allow them to
accurately and reliably reason about real-world phenomena, and they can adapt
such models extremely quickly in the face of unexpected changes. Deep neural
network models allow us to represent very complex functions, but lack this
capacity for rapid online adaptation. The goal in this paper is to develop a
method for continual online learning from an incoming stream of data, using
deep neural network models. We formulate an online learning procedure that uses
stochastic gradient descent to update model parameters, and an expectation
maximization algorithm with a Chinese restaurant process prior to develop and
maintain a mixture of models to handle non-stationary task distributions. This
allows for all models to be adapted as necessary, with new models instantiated
for task changes and old models recalled when previously seen tasks are
encountered again. Furthermore, we observe that meta-learning can be used to
meta-train a model such that this direct online adaptation with SGD is
effective, which is otherwise not the case for large function approximators. In
this work, we apply our meta-learning for online learning (MOLe) approach to
model-based reinforcement learning, where adapting the predictive model is
critical for control; we demonstrate that MOLe outperforms alternative prior
methods, and enables effective continuous adaptation in non-stationary task
distributions such as varying terrains, motor failures, and unexpected
disturbances.Comment: Project website: https://sites.google.com/berkeley.edu/onlineviamet
Continuous Time Markov Networks
A central task in many applications is reasoning about processes that change
in a continuous time. The mathematical framework of Continuous Time Markov
Processes provides the basic foundations for modeling such systems. Recently,
Nodelman et al introduced continuous time Bayesian networks (CTBNs), which
allow a compact representation of continuous-time processes over a factored
state space. In this paper, we introduce continuous time Markov networks
(CTMNs), an alternative representation language that represents a different
type of continuous-time dynamics. In many real life processes, such as
biological and chemical systems, the dynamics of the process can be naturally
described as an interplay between two forces - the tendency of each entity to
change its state, and the overall fitness or energy function of the entire
system. In our model, the first force is described by a continuous-time
proposal process that suggests possible local changes to the state of the
system at different rates. The second force is represented by a Markov network
that encodes the fitness, or desirability, of different states; a proposed
local change is then accepted with a probability that is a function of the
change in the fitness distribution. We show that the fitness distribution is
also the stationary distribution of the Markov process, so that this
representation provides a characterization of a temporal process whose
stationary distribution has a compact graphical representation. This allows us
to naturally capture a different type of structure in complex dynamical
processes, such as evolving biological sequences. We describe the semantics of
the representation, its basic properties, and how it compares to CTBNs. We also
provide algorithms for learning such models from data, and discuss its
applicability to biological sequence evolution.Comment: Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006
Variational Bayesian Inference for Audio-Visual Tracking of Multiple Speakers
In this paper we address the problem of tracking multiple speakers via the
fusion of visual and auditory information. We propose to exploit the
complementary nature of these two modalities in order to accurately estimate
smooth trajectories of the tracked persons, to deal with the partial or total
absence of one of the modalities over short periods of time, and to estimate
the acoustic status -- either speaking or silent -- of each tracked person
along time. We propose to cast the problem at hand into a generative
audio-visual fusion (or association) model formulated as a latent-variable
temporal graphical model. This may well be viewed as the problem of maximizing
the posterior joint distribution of a set of continuous and discrete latent
variables given the past and current observations, which is intractable. We
propose a variational inference model which amounts to approximate the joint
distribution with a factorized distribution. The solution takes the form of a
closed-form expectation maximization procedure. We describe in detail the
inference algorithm, we evaluate its performance and we compare it with several
baseline methods. These experiments show that the proposed audio-visual tracker
performs well in informal meetings involving a time-varying number of people
Large Scale Estimation in Cyberphysical Systems using Streaming Data: a Case Study with Smartphone Traces
Controlling and analyzing cyberphysical and robotics systems is increasingly
becoming a Big Data challenge. Pushing this data to, and processing in the
cloud is more efficient than on-board processing. However, current cloud-based
solutions are not suitable for the latency requirements of these applications.
We present a new concept, Discretized Streams or D-Streams, that enables
massively scalable computations on streaming data with latencies as short as a
second.
We experiment with an implementation of D-Streams on top of the Spark
computing framework. We demonstrate the usefulness of this concept with a novel
algorithm to estimate vehicular traffic in urban networks. Our online EM
algorithm can estimate traffic on a very large city network (the San Francisco
Bay Area) by processing tens of thousands of observations per second, with a
latency of a few seconds
Compositional Stochastic Modeling and Probabilistic Programming
Probabilistic programming is related to a compositional approach to
stochastic modeling by switching from discrete to continuous time dynamics. In
continuous time, an operator-algebra semantics is available in which processes
proceeding in parallel (and possibly interacting) have summed time-evolution
operators. From this foundation, algorithms for simulation, inference and model
reduction may be systematically derived. The useful consequences are
potentially far-reaching in computational science, machine learning and beyond.
Hybrid compositional stochastic modeling/probabilistic programming approaches
may also be possible.Comment: Extended Abstract for the Neural Information Processing Systems
(NIPS) Workshop on Probabilistic Programming, 201
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