15,656 research outputs found
A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
The benefits of automating design cycles for Bayesian inference-based
algorithms are becoming increasingly recognized by the machine learning
community. As a result, interest in probabilistic programming frameworks has
much increased over the past few years. This paper explores a specific
probabilistic programming paradigm, namely message passing in Forney-style
factor graphs (FFGs), in the context of automated design of efficient Bayesian
signal processing algorithms. To this end, we developed "ForneyLab"
(https://github.com/biaslab/ForneyLab.jl) as a Julia toolbox for message
passing-based inference in FFGs. We show by example how ForneyLab enables
automatic derivation of Bayesian signal processing algorithms, including
algorithms for parameter estimation and model comparison. Crucially, due to the
modular makeup of the FFG framework, both the model specification and inference
methods are readily extensible in ForneyLab. In order to test this framework,
we compared variational message passing as implemented by ForneyLab with
automatic differentiation variational inference (ADVI) and Monte Carlo methods
as implemented by state-of-the-art tools "Edward" and "Stan". In terms of
performance, extensibility and stability issues, ForneyLab appears to enjoy an
edge relative to its competitors for automated inference in state-space models.Comment: Accepted for publication in the International Journal of Approximate
Reasonin
A dynamic Bayesian nonlinear mixed-effects model of HIV response incorporating medication adherence, drug resistance and covariates
HIV dynamic studies have contributed significantly to the understanding of
HIV pathogenesis and antiviral treatment strategies for AIDS patients.
Establishing the relationship of virologic responses with clinical factors and
covariates during long-term antiretroviral (ARV) therapy is important to the
development of effective treatments. Medication adherence is an important
predictor of the effectiveness of ARV treatment, but an appropriate determinant
of adherence rate based on medication event monitoring system (MEMS) data is
critical to predict virologic outcomes. The primary objective of this paper is
to investigate the effects of a number of summary determinants of MEMS
adherence rates on virologic response measured repeatedly over time in
HIV-infected patients. We developed a mechanism-based differential equation
model with consideration of drug adherence, interacted by virus susceptibility
to drug and baseline characteristics, to characterize the long-term virologic
responses after initiation of therapy. This model fully integrates viral load,
MEMS adherence, drug resistance and baseline covariates into the data analysis.
In this study we employed the proposed model and associated Bayesian nonlinear
mixed-effects modeling approach to assess how to efficiently use the MEMS
adherence data for prediction of virologic response, and to evaluate the
predicting power of each summary metric of the MEMS adherence rates.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS376 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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