28,765 research outputs found
Hybrid Pathwise Sensitivity Methods for Discrete Stochastic Models of Chemical Reaction Systems
Stochastic models are often used to help understand the behavior of
intracellular biochemical processes. The most common such models are continuous
time Markov chains (CTMCs). Parametric sensitivities, which are derivatives of
expectations of model output quantities with respect to model parameters, are
useful in this setting for a variety of applications. In this paper, we
introduce a class of hybrid pathwise differentiation methods for the numerical
estimation of parametric sensitivities. The new hybrid methods combine elements
from the three main classes of procedures for sensitivity estimation, and have
a number of desirable qualities. First, the new methods are unbiased for a
broad class of problems. Second, the methods are applicable to nearly any
physically relevant biochemical CTMC model. Third, and as we demonstrate on
several numerical examples, the new methods are quite efficient, particularly
if one wishes to estimate the full gradient of parametric sensitivities. The
methods are rather intuitive and utilize the multilevel Monte Carlo philosophy
of splitting an expectation into separate parts and handling each in an
efficient manner.Comment: 30 pages. The numerical example section has been extensively
rewritte
A New Optimal Stepsize For Approximate Dynamic Programming
Approximate dynamic programming (ADP) has proven itself in a wide range of
applications spanning large-scale transportation problems, health care, revenue
management, and energy systems. The design of effective ADP algorithms has many
dimensions, but one crucial factor is the stepsize rule used to update a value
function approximation. Many operations research applications are
computationally intensive, and it is important to obtain good results quickly.
Furthermore, the most popular stepsize formulas use tunable parameters and can
produce very poor results if tuned improperly. We derive a new stepsize rule
that optimizes the prediction error in order to improve the short-term
performance of an ADP algorithm. With only one, relatively insensitive tunable
parameter, the new rule adapts to the level of noise in the problem and
produces faster convergence in numerical experiments.Comment: Matlab files are included with the paper sourc
How close are time series to power tail L\'evy diffusions?
This article presents a new and easily implementable method to quantify the
so-called coupling distance between the law of a time series and the law of a
differential equation driven by Markovian additive jump noise with heavy-tailed
jumps, such as -stable L\'evy flights. Coupling distances measure the
proximity of the empirical law of the tails of the jump increments and a given
power law distribution. In particular they yield an upper bound for the
distance of the respective laws on path space. We prove rates of convergence
comparable to the rates of the central limit theorem which are confirmed by
numerical simulations. Our method applied to a paleoclimate time series of
glacial climate variability confirms its heavy tail behavior. In addition this
approach gives evidence for heavy tails in data sets of precipitable water
vapor of the Western Tropical Pacific.Comment: 30 pages, 10 figure
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