3,364 research outputs found
Bisimulation Relations Between Automata, Stochastic Differential Equations and Petri Nets
Two formal stochastic models are said to be bisimilar if their solutions as a
stochastic process are probabilistically equivalent. Bisimilarity between two
stochastic model formalisms means that the strengths of one stochastic model
formalism can be used by the other stochastic model formalism. The aim of this
paper is to explain bisimilarity relations between stochastic hybrid automata,
stochastic differential equations on hybrid space and stochastic hybrid Petri
nets. These bisimilarity relations make it possible to combine the formal
verification power of automata with the analysis power of stochastic
differential equations and the compositional specification power of Petri nets.
The relations and their combined strengths are illustrated for an air traffic
example.Comment: 15 pages, 4 figures, Workshop on Formal Methods for Aerospace (FMA),
EPTCS 20m 201
Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks
We apply a multilayer perceptron machine learning (ML) regression approach to
infer electromagnetic (EM) duct heights within the marine atmospheric boundary
layer (MABL) using sparsely sampled EM propagation data obtained within a
bistatic context. This paper explains the rationale behind the selection of the
ML network architecture, along with other model hyperparameters, in an effort
to demystify the process of arriving at a useful ML model. The resulting speed
of our ML predictions of EM duct heights, using sparse data measurements within
MABL, indicates the suitability of the proposed method for real-time
applications.Comment: 13 pages, 7 figure
The History of the Quantitative Methods in Finance Conference Series. 1992-2007
This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.
Learning and testing stochastic discrete event
Dissertação de mestrado em Engenharia de InformáticaSistemas de eventos discretos (DES) são uma importante subclasse de sistemas (à luz da teoria dos sistemas). Estes têm sido usados, particularmente na indústria para analisar e modelar um vasto conjunto de sistemas reais, tais como, sistemas de produção, sistemas de computador, sistemas de controlo de tráfego e sistemas híbridos.
O nosso trabalho explora uma extensão de DES com ênfase nos processos estocásticos, comummente chamado como sistemas de eventos discretos estocásticos (SDES). Existe assim a necessidade de estabelecer uma abstração estocástica através do uso de processos semi-Markovianos generalizados (GSMP) para SDES.
Assim, o objetivo do nosso trabalho é propor uma metodologia e um conjunto de algoritmos para aprendizagem de GSMP, usar técnicas de model-checking estatístico para a verificação e propor duas novas abordagens para teste de DES e SDES (respetivamente, não estocasticamente e estocasticamente).
Este trabalho também introduz uma noção de modelação, analise e verificação de sistemas contínuos e modelos de perturbação no contexto da verificação por model-checking estatístico.Discrete event systems (DES) are an important subclass of systems (in systems theory). They have been used, particularly in industry, to analyze and model a wide variety of real systems, such as production systems, computer systems, traffic systems, and hybrid systems. Our work explores an extension of DES with an emphasis on stochastic processes, commonly called stochastic discrete event systems (SDES). There was a need to establish a stochastic abstraction for SDES through generalized semi-Markov processes (GSMP). Thus, the aim of our work is to propose a methodology and a set of algorithms for GSMP learning, using model checking techniques for verification, and to propose two new approaches for testing DES and SDES (non-stochastically and stochastically). This work also introduces a notion of modeling, analysis, and verification of continuous systems and disturbance models in the context of verifiable statistical model checking
Recommended from our members
Linear state models for volatility estimation and prediction
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis concerns the calibration and estimation of linear state models for forecasting stock return volatility. In the first two chapters I present aspects of financial modelling theory and practice that are of particular relevance to the theme of this present work. In addition to this I
review the literature concerning these aspects with a particular emphasis on the area of dynamic volatility models. These chapters set the scene and lay the foundations for subsequent empirical work and are a contribution in themselves. The structure of the models employed in the application chapters 4,5 and 6 is the state-space structure, or alternatively the models are known as unobserved components models. In the literature these models have been applied in the estimation of volatility, both for high frequency and low frequency data. As opposed to what has been carried out in the literature I propose the use of these models with Gaussian components. I suggest the implementation of these for high frequency data for short and medium term forecasting. I then demonstrate the calibration of these models and compare medium term forecasting performance for different forecasting methods and model
variations as well as that of GARCH and constant volatility models. I then introduce implied volatility measurements leading to two-state models and verify whether this derivative-based information improves forecasting performance. In chapter 6I compare different unobserved components models' specification and forecasting performance. The appendices contain the
extensive workings of the parameter estimates' standard error calculations
Automatic differentiation in machine learning: a survey
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in
machine learning. Automatic differentiation (AD), also called algorithmic
differentiation or simply "autodiff", is a family of techniques similar to but
more general than backpropagation for efficiently and accurately evaluating
derivatives of numeric functions expressed as computer programs. AD is a small
but established field with applications in areas including computational fluid
dynamics, atmospheric sciences, and engineering design optimization. Until very
recently, the fields of machine learning and AD have largely been unaware of
each other and, in some cases, have independently discovered each other's
results. Despite its relevance, general-purpose AD has been missing from the
machine learning toolbox, a situation slowly changing with its ongoing adoption
under the names "dynamic computational graphs" and "differentiable
programming". We survey the intersection of AD and machine learning, cover
applications where AD has direct relevance, and address the main implementation
techniques. By precisely defining the main differentiation techniques and their
interrelationships, we aim to bring clarity to the usage of the terms
"autodiff", "automatic differentiation", and "symbolic differentiation" as
these are encountered more and more in machine learning settings.Comment: 43 pages, 5 figure
- …