90,523 research outputs found
Challenges in Stochastic Programming
Remarkable progress has been made in the development of algorithmic procedures and the availability of software for stochastic programming problems. However, some fundamental questions have remained unexplored. This paper identifies the more challenging open questions in the field of stochastic programming. Some are purely technical in nature, but many also go to the foundations of designing models for decision making under uncertainty
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabilistic
models implicitly defined by stochastic generative models of data. In
scientific fields ranging from population biology to cosmology, low-level
mechanistic components are composed to create complex generative models. These
models lead to intractable likelihoods and are typically non-differentiable,
which poses challenges for traditional approaches to inference. We extend
previous work in "inference compilation", which combines universal
probabilistic programming and deep learning methods, to large-scale scientific
simulators, and introduce a C++ based probabilistic programming library called
CPProb. We successfully use CPProb to interface with SHERPA, a large code-base
used in particle physics. Here we describe the technical innovations realized
and planned for this library.Comment: 7 pages, 2 figure
BlenX-based compositional modeling of complex reaction mechanisms
Molecular interactions are wired in a fascinating way resulting in complex
behavior of biological systems. Theoretical modeling provides a useful
framework for understanding the dynamics and the function of such networks. The
complexity of the biological networks calls for conceptual tools that manage
the combinatorial explosion of the set of possible interactions. A suitable
conceptual tool to attack complexity is compositionality, already successfully
used in the process algebra field to model computer systems. We rely on the
BlenX programming language, originated by the beta-binders process calculus, to
specify and simulate high-level descriptions of biological circuits. The
Gillespie's stochastic framework of BlenX requires the decomposition of
phenomenological functions into basic elementary reactions. Systematic
unpacking of complex reaction mechanisms into BlenX templates is shown in this
study. The estimation/derivation of missing parameters and the challenges
emerging from compositional model building in stochastic process algebras are
discussed. A biological example on circadian clock is presented as a case study
of BlenX compositionality
On Designing Multicore-aware Simulators for Biological Systems
The stochastic simulation of biological systems is an increasingly popular
technique in bioinformatics. It often is an enlightening technique, which may
however result in being computational expensive. We discuss the main
opportunities to speed it up on multi-core platforms, which pose new challenges
for parallelisation techniques. These opportunities are developed in two
general families of solutions involving both the single simulation and a bulk
of independent simulations (either replicas of derived from parameter sweep).
Proposed solutions are tested on the parallelisation of the CWC simulator
(Calculus of Wrapped Compartments) that is carried out according to proposed
solutions by way of the FastFlow programming framework making possible fast
development and efficient execution on multi-cores.Comment: 19 pages + cover pag
Spatial stochastic programming model for timber and core area management under risk of stand-replacing fire, A
2012 Fall.Includes bibliographical references.Forest harvest scheduling has been modeled using deterministic and stochastic programming models. Past models seldom address explicit spatial forest management concerns under the influence of natural disturbances. In this research study, we employ multistage full recourse stochastic programming models to explore the challenges and advantages of building spatial optimization models that account for the influences of random stand-replacing fires. Our exploratory test models simultaneously consider timber harvest and mature forest core area objectives. Each model run reports first-period harvesting decisions for each stand based on a sample set of random fire. We integrate multiple model runs to evaluate the persistence of period-one solutions under the influence of stochastic fires. Follow-up simulations were used to support multiple comparisons of different candidate forest management alternatives for the first time period. Test case results indicate that integrating the occurrence of stand-replacing fire into forest harvest scheduling models could improve the quality of long-term spatially explicit forest plans
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