17,829 research outputs found
A Method for the Combination of Stochastic Time Varying Load Effects
The problem of evaluating the probability that a structure becomes unsafe under a
combination of loads, over a given time period, is addressed. The loads and load effects
are modeled as either pulse (static problem) processes with random occurrence time, intensity and a specified shape or intermittent continuous (dynamic problem) processes which
are zero mean Gaussian processes superimposed 'on a pulse process. The load coincidence
method is extended to problems with both nonlinear limit states and dynamic responses,
including the case of correlated dynamic responses. The technique of linearization of a
nonlinear limit state commonly used in a time-invariant problem is investigated for timevarying
combination problems, with emphasis on selecting the linearization point. Results
are compared with other methods, namely the method based on upcrossing rate, simpler
combination rules such as Square Root of Sum of Squares and Turkstra's rule. Correlated
effects among dynamic loads are examined to see how results differ from correlated static
loads and to demonstrate which types of load dependencies are most important, i.e., affect'
the exceedance probabilities the most.
Application of the load coincidence method to code development is briefly discussed.National Science Foundation Grants CME 79-18053 and CEE 82-0759
Fluctuations in glassy systems
We summarize a theoretical framework based on global time-reparametrization
invariance that explains the origin of dynamic fluctuations in glassy systems.
We introduce the main ideas without getting into much technical details. We
describe a number of consequences arising from this scenario that can be tested
numerically and experimentally distinguishing those that can also be explained
by other mechanisms from the ones that we believe, are special to our proposal.
We support our claims by presenting some numerical checks performed on the 3d
Edwards-Anderson spin-glass. Finally, we discuss up to which extent these ideas
apply to super-cooled liquids that have been studied in much more detail up to
present.Comment: 33 pages, 7 figs, contribution to JSTAT special issue `Principles of
Dynamical Systems' work-shop at Newton Institute, Univ. of Cambridge, U
Sequential Monte Carlo EM for multivariate probit models
Multivariate probit models (MPM) have the appealing feature of capturing some
of the dependence structure between the components of multidimensional binary
responses. The key for the dependence modelling is the covariance matrix of an
underlying latent multivariate Gaussian. Most approaches to MLE in multivariate
probit regression rely on MCEM algorithms to avoid computationally intensive
evaluations of multivariate normal orthant probabilities. As an alternative to
the much used Gibbs sampler a new SMC sampler for truncated multivariate
normals is proposed. The algorithm proceeds in two stages where samples are
first drawn from truncated multivariate Student distributions and then
further evolved towards a Gaussian. The sampler is then embedded in a MCEM
algorithm. The sequential nature of SMC methods can be exploited to design a
fully sequential version of the EM, where the samples are simply updated from
one iteration to the next rather than resampled from scratch. Recycling the
samples in this manner significantly reduces the computational cost. An
alternative view of the standard conditional maximisation step provides the
basis for an iterative procedure to fully perform the maximisation needed in
the EM algorithm. The identifiability of MPM is also thoroughly discussed. In
particular, the likelihood invariance can be embedded in the EM algorithm to
ensure that constrained and unconstrained maximisation are equivalent. A simple
iterative procedure is then derived for either maximisation which takes
effectively no computational time. The method is validated by applying it to
the widely analysed Six Cities dataset and on a higher dimensional simulated
example. Previous approaches to the Six Cities overly restrict the parameter
space but, by considering the correct invariance, the maximum likelihood is
quite naturally improved when treating the full unrestricted model.Comment: 26 pages, 2 figures. In press, Computational Statistics & Data
Analysi
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