12,548 research outputs found
Probabilistic graphical models parameter learning with transferred prior and constraints
Learning accurate Bayesian networks (BNs) is a key challenge in real-world applications, especially when training data are hard to acquire. Two approaches have been used to address this challenge: 1) introducing expert judgements and 2) transferring knowledge from related domains. This is the first paper to present a generic framework that combines both approaches to improve BN parameter learning. This framework is built upon an extended multinomial parameter learning model, that itself is an auxiliary BN. It serves to integrate both knowledge transfer and expert constraints. Experimental results demonstrate improved accuracy of the new method on a variety of benchmark BNs, showing its potential to benefit many real-world problems
Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization
With the introduction of spectral-domain optical coherence tomography (OCT),
resulting in a significant increase in acquisition speed, the fast and accurate
segmentation of 3-D OCT scans has become evermore important. This paper
presents a novel probabilistic approach, that models the appearance of retinal
layers as well as the global shape variations of layer boundaries. Given an OCT
scan, the full posterior distribution over segmentations is approximately
inferred using a variational method enabling efficient probabilistic inference
in terms of computationally tractable model components: Segmenting a full 3-D
volume takes around a minute. Accurate segmentations demonstrate the benefit of
using global shape regularization: We segmented 35 fovea-centered 3-D volumes
with an average unsigned error of 2.46 0.22 {\mu}m as well as 80 normal
and 66 glaucomatous 2-D circular scans with errors of 2.92 0.53 {\mu}m
and 4.09 0.98 {\mu}m respectively. Furthermore, we utilized the inferred
posterior distribution to rate the quality of the segmentation, point out
potentially erroneous regions and discriminate normal from pathological scans.
No pre- or postprocessing was required and we used the same set of parameters
for all data sets, underlining the robustness and out-of-the-box nature of our
approach.Comment: Accepted for publication in Medical Image Analysis (MIA), Elsevie
Receiver Architectures for MIMO-OFDM Based on a Combined VMP-SP Algorithm
Iterative information processing, either based on heuristics or analytical
frameworks, has been shown to be a very powerful tool for the design of
efficient, yet feasible, wireless receiver architectures. Within this context,
algorithms performing message-passing on a probabilistic graph, such as the
sum-product (SP) and variational message passing (VMP) algorithms, have become
increasingly popular.
In this contribution, we apply a combined VMP-SP message-passing technique to
the design of receivers for MIMO-ODFM systems. The message-passing equations of
the combined scheme can be obtained from the equations of the stationary points
of a constrained region-based free energy approximation. When applied to a
MIMO-OFDM probabilistic model, we obtain a generic receiver architecture
performing iterative channel weight and noise precision estimation,
equalization and data decoding. We show that this generic scheme can be
particularized to a variety of different receiver structures, ranging from
high-performance iterative structures to low complexity receivers. This allows
for a flexible design of the signal processing specially tailored for the
requirements of each specific application. The numerical assessment of our
solutions, based on Monte Carlo simulations, corroborates the high performance
of the proposed algorithms and their superiority to heuristic approaches
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