1,110 research outputs found
Monte Carlo Algorithms for the Partition Function and Information Rates of Two-Dimensional Channels
The paper proposes Monte Carlo algorithms for the computation of the
information rate of two-dimensional source/channel models. The focus of the
paper is on binary-input channels with constraints on the allowed input
configurations. The problem of numerically computing the information rate, and
even the noiseless capacity, of such channels has so far remained largely
unsolved. Both problems can be reduced to computing a Monte Carlo estimate of a
partition function. The proposed algorithms use tree-based Gibbs sampling and
multilayer (multitemperature) importance sampling. The viability of the
proposed algorithms is demonstrated by simulation results
Factor Graphs for Quantum Probabilities
A factor-graph representation of quantum-mechanical probabilities (involving
any number of measurements) is proposed. Unlike standard statistical models,
the proposed representation uses auxiliary variables (state variables) that are
not random variables. All joint probability distributions are marginals of some
complex-valued function , and it is demonstrated how the basic concepts of
quantum mechanics relate to factorizations and marginals of .Comment: To appear in IEEE Transactions on Information Theory, 201
A Factor-Graph Representation of Probabilities in Quantum Mechanics
A factor-graph representation of quantum-mechanical probabilities is
proposed. Unlike standard statistical models, the proposed representation uses
auxiliary variables (state variables) that are not random variables.Comment: Proc. IEEE International Symposium on Information Theory (ISIT),
Cambridge, MA, July 1-6, 201
LMMSE Estimation and Interpolation of Continuous-Time Signals from Discrete-Time Samples Using Factor Graphs
The factor graph approach to discrete-time linear Gaussian state space models
is well developed. The paper extends this approach to continuous-time linear
systems/filters that are driven by white Gaussian noise. By Gaussian message
passing, we then obtain MAP/MMSE/LMMSE estimates of the input signal, or of the
state, or of the output signal from noisy observations of the output signal.
These estimates may be obtained with arbitrary temporal resolution. The
proposed input signal estimation does not seem to have appeared in the prior
Kalman filtering literature
Model-Predictive Control with New NUV Priors
Normals with unknown variance (NUV) can represent many useful priors
including norms and other sparsifying priors, and they blend well with
linear-Gaussian models and Gaussian message passing algorithms. In this paper,
we elaborate on recently proposed discretizing NUV priors, and we propose new
NUV representations of half-space constraints and box constraints. We then
demonstrate the use of such NUV representations with exemplary applications in
model predictive control, with a variety of constraints on the input, the
output, or the internal stateof the controlled system. In such applications,
the computations boil down to iterations of Kalman-type forward-backward
recursions, with a complexity (per iteration) that is linear in the planning
horizon. In consequence, this approach can handle long planning horizons, which
distinguishes it from the prior art. For nonconvex constraints, this approach
has no claim to optimality, but it is empirically very effective
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