2,051 research outputs found
Aggregated Gradient Langevin Dynamics
In this paper, we explore a general Aggregated Gradient Langevin Dynamics
framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling. We
investigate the nonasymptotic convergence of AGLD with a unified analysis for
different data accessing (e.g. random access, cyclic access and random
reshuffle) and snapshot updating strategies, under convex and nonconvex
settings respectively. It is the first time that bounds for I/O friendly
strategies such as cyclic access and random reshuffle have been established in
the MCMC literature. The theoretic results also indicate that methods in AGLD
possess the merits of both the low per-iteration computational complexity and
the short mixture time. Empirical studies demonstrate that our framework allows
to derive novel schemes to generate high-quality samples for large-scale
Bayesian posterior learning tasks
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Substrate-Specific Inhibition Constants for Phospholipase A2 Acting on Unique Phospholipid Substrates in Mixed Micelles and Membranes Using Lipidomics.
Assaying lipolytic enzymes is extremely challenging because they act on water-insoluble lipid substrates, which are normally components of micelles, vesicles, and cellular membranes. We extended a new lipidomics-based liquid chromatographic-mass spectrometric assay for phospholipases A2 to perform inhibition analysis using a variety of commercially available synthetic and natural phospholipids as substrates. Potent and selective inhibitors of three recombinant human enzymes, including cytosolic, calcium-independent, and secreted phospholipases A2 were used to establish and validate this assay. This is a novel use of dose-response curves with a mixture of phospholipid substrates, not previously feasible using traditional radioactive assays. The new application of lipidomics to developing assays for lipolytic enzymes revolutionizes in vitro testing for the discovery of potent and selective inhibitors using mixtures of membranelike substrates
Space-Time Continuous Models of Swarm Robotic Systems: Supporting Global-to-Local Programming
A generic model in as far as possible mathematical closed-form was developed that predicts the behavior of large self-organizing robot groups (robot swarms) based on their control algorithm. In addition, an extensive subsumption of the relatively young and distinctive interdisciplinary research field of swarm robotics is emphasized. The connection to many related fields is highlighted and the concepts and methods borrowed from these fields are described shortly
Patterns of Scalable Bayesian Inference
Datasets are growing not just in size but in complexity, creating a demand
for rich models and quantification of uncertainty. Bayesian methods are an
excellent fit for this demand, but scaling Bayesian inference is a challenge.
In response to this challenge, there has been considerable recent work based on
varying assumptions about model structure, underlying computational resources,
and the importance of asymptotic correctness. As a result, there is a zoo of
ideas with few clear overarching principles.
In this paper, we seek to identify unifying principles, patterns, and
intuitions for scaling Bayesian inference. We review existing work on utilizing
modern computing resources with both MCMC and variational approximation
techniques. From this taxonomy of ideas, we characterize the general principles
that have proven successful for designing scalable inference procedures and
comment on the path forward
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