4,561 research outputs found
Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art
Stochasticity is a key characteristic of intracellular processes such as gene
regulation and chemical signalling. Therefore, characterising stochastic
effects in biochemical systems is essential to understand the complex dynamics
of living things. Mathematical idealisations of biochemically reacting systems
must be able to capture stochastic phenomena. While robust theory exists to
describe such stochastic models, the computational challenges in exploring
these models can be a significant burden in practice since realistic models are
analytically intractable. Determining the expected behaviour and variability of
a stochastic biochemical reaction network requires many probabilistic
simulations of its evolution. Using a biochemical reaction network model to
assist in the interpretation of time course data from a biological experiment
is an even greater challenge due to the intractability of the likelihood
function for determining observation probabilities. These computational
challenges have been subjects of active research for over four decades. In this
review, we present an accessible discussion of the major historical
developments and state-of-the-art computational techniques relevant to
simulation and inference problems for stochastic biochemical reaction network
models. Detailed algorithms for particularly important methods are described
and complemented with MATLAB implementations. As a result, this review provides
a practical and accessible introduction to computational methods for stochastic
models within the life sciences community
Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals
Reconstruction of the tridimensional geometry of a visual scene using the
binocular disparity information is an important issue in computer vision and
mobile robotics, which can be formulated as a Bayesian inference problem.
However, computation of the full disparity distribution with an advanced
Bayesian model is usually an intractable problem, and proves computationally
challenging even with a simple model. In this paper, we show how probabilistic
hardware using distributed memory and alternate representation of data as
stochastic bitstreams can solve that problem with high performance and energy
efficiency. We put forward a way to express discrete probability distributions
using stochastic data representations and perform Bayesian fusion using those
representations, and show how that approach can be applied to diparity
computation. We evaluate the system using a simulated stochastic implementation
and discuss possible hardware implementations of such architectures and their
potential for sensorimotor processing and robotics.Comment: Preprint of article submitted for publication in International
Journal of Approximate Reasoning and accepted pending minor revision
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