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作者： Dan Koskenniemi Aalto University

学位级别：硕士

The purpose of this thesis is to investigate if modifications to the Small-Minus-Big and High-Minus-Low factors in the Fama-French three-factor model (FF3) will improve the models explanatory power in the North American stock market for the period 2000-2015, utilizing data from Wharton Research Data Centre (WRDS). The modifications of the factors are carried out by a reconstruction of the six portfolios of stocks in the SMB (Small-Minus-Big) and HML (High-Minus-Low) factors, utilizing a combination of stock correlation networks and label propagation algorithms. The theory part of the thesis is split into two major topics, the rationale and methods behind the applied investment theory and the problems that lead to further development of the Fama French three factor model, and a semi-supervised learning approach to the reconstruction of the underlying six portfolios in the Fama French three factor model. The empirical tests show that the standard Fama French three factor model does not hold a high level of explanatory power, and the graph-based semi-supervised learning approach was only able to outperform the standard FF3 model, when basic statistical tests pruned out stocks not being coherent with the theoretical description of the FF3 model.

关键词： factor reconstruction Fama French 3 factor model graph signal recovery graph-based semi-supervised learning HML propagation algorithms SMB stock correlation network

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Practical numerical propagation of arbitrary wavefronts through PIAA optics

Practical numerical propagation of arbitrary wavefronts thro...

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Conference on Space Telescopes and Instrumentation 2010 - Optical, Infrared, and Millimeter Wave

作者： Krist, John E. Pueyo, Laurent Shaklan, Stuart B. CALTECH Jet Prop Lab Pasadena CA 91109 USA

ISBN: (纸本)9780819482211

The phase-induced amplitude apodization (PIAA) coronagraph utilizes highly aspheric optics to produce a strongly apodized beam without the large loss of light that would result from using a graded transmission mask. The rapid variations in surface curvature at the edge of the PIAA apodizing optic creates large wavefront phase changes that cannot be adequately represented in conventional Fourier-based diffraction propagation algorithms. A rapid technique is required for propagating arbitrarily-aberrated wavefronts through the system. An alternative numerical method has been proposed that combines a high-accuracy algorithm to compute edge diffraction effects with a quick modified angular spectrum propagator that handles wavefront errors. We present the results of applying this method to realistically aberrated wavefronts as compared to more complex and time consuming techniques.

关键词： Coronagraph PIAA propagation algorithms

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Using probability trees to compute marginals with imprecise probabilities

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2002年第1期29卷 1-46页

作者： Cano, A Moral, S Univ Granada Dept Comp Sci & Artificial Intelligence ETS Ingn Informat Granada 18071 Spain

This paper presents an approximate algorithm to obtain a posteriori intervals of probability, when available information is also given with intervals. The algorithm uses probability trees as a means of representing and computing with the convex sets of probabilities associated to the intervals. (C) 2002 Elsevier Science Inc. All rights reserved.

关键词： dependence graphs interval probabilities propagation algorithms probability trees

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Filtering algorithms for the multiset ordering constraint

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ARTIFICIAL INTELLIGENCE 2009年第2期173卷 299-328页

作者： Frisch, Alan M. Hnich, Brahim Kiziltan, Zeynep Miguel, Ian Walsh, Toby Univ Bologna Dept Comp Sci I-40126 Bologna Italy Univ York Dept Comp Sci York YO10 5DD N Yorkshire England Izmir Univ Econ Fac Comp Sci Izmir Turkey Univ St Andrews Sch Comp Sci St Andrews KY16 9AJ Fife Scotland Univ New S Wales Sch Engn & Comp Sci Sydney NSW 2052 Australia

Constraint programming (CP) has been used with great success to tackle a wide variety of constraint satisfaction problems which are computationally intractable in general. Global constraints are one of the important factors behind the success of CP. In this paper, we study a new global constraint, the multiset ordering constraint, which is shown to be useful in symmetry breaking and searching for leximin optimal solutions in CP. We propose efficient and effective filtering algorithms for propagating this global constraint. We show that the algorithms maintain generalised arc-consistency and we discuss possible extensions. We also consider alternative propagation methods based on existing constraints in CP toolkits. Our experimental results on a number of benchmark problems demonstrate that propagating the multiset ordering constraint via a dedicated algorithm can be very beneficial. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Constraint satisfaction Constraint programming Modelling Global constraints Constraint propagation propagation algorithms Symmetry breaking Multiset ordering Leximin optimal solutions

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Generating custom propagators for arbitrary constraints

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ARTIFICIAL INTELLIGENCE 2014年第1期211卷 1-33页

作者： Gent, Ian P. Jefferson, Christopher Linton, Steve Miguel, Ian Nightingale, Peter Univ St Andrews Sch Comp Sci St Andrews KY16 9SX Fife Scotland

Constraint Programming (CP) is a proven set of techniques for solving complex combinatorial problems from a range of disciplines. The problem is specified as a set of decision variables (with finite domains) and constraints linking the variables. Local reasoning (propagation) on the constraints is central to CP. Many constraints have efficient constraint-specific propagation algorithms. In this work, we generate custom propagators for constraints. These custom propagators can be very efficient, even approaching (and in some cases exceeding) the efficiency of hand-optimised propagators. Given an arbitrary constraint, we show how to generate a custom propagator that establishes GAC in small polynomial time. This is done by precomputing the propagation that would be performed on every relevant subdomain. The number of relevant subdomains, and therefore the size of the generated propagator, is potentially exponential in the number and domain size of the constrained variables. The limiting factor of our approach is the size of the generated propagators. We investigate symmetry as a means of reducing that size. We exploit the symmetries of the constraint to merge symmetric parts of the generated propagator. This extends the reach of our approach to somewhat larger constraints, with a small run-time penalty. Our experimental results show that, compared with optimised implementations of the table constraint, our techniques can lead to an order of magnitude speedup. propagation is so fast that the generated propagators compare well with hand-written carefully optimised propagators for the same constraints, and the time taken to generate a propagator is more than repaid. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license

关键词： Constraint programming Constraint satisfaction problem propagation algorithms Combinatorial search

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The extended global cardinality constraint: An empirical survey

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ARTIFICIAL INTELLIGENCE 2011年第2期175卷 586-614页

作者： Nightingale, Peter Univ St Andrews Sch Comp Sci St Andrews KY16 9SX Fife Scotland

The Extended Global Cardinality Constraint (EGCC) is a vital component of constraint solving systems, since it is very widely used to model diverse problems. The literature contains many different versions of this constraint, which trade strength of inference against computational cost. In this paper, I focus on the highest strength of inference usually considered, enforcing generalized arc consistency (GAC) on the target variables. This work is an extensive empirical survey of algorithms and optimizations, considering both GAC on the target variables, and tightening the bounds of the cardinality variables. I evaluate a number of key techniques from the literature, and report important implementation details of those techniques, which have often not been described in published papers. Two new optimizations are proposed for EGCC. One of the novel optimizations (dynamic partitioning, generalized from AllDifferent) was found to speed up search by 5.6 times in the best case and 1.56 times on average, while exploring the same search tree. The empirical work represents by far the most extensive set of experiments on variants of algorithms for EGCC. Overall, the best combination of optimizations gives a mean speedup of 4.11 times compared to the same implementation without the optimizations. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Global cardinality constraint Constraint programming Global constraints propagation algorithms

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A new multi-objectives approach to implement preventive and protective barriers in bow tie diagram

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JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES 2014年 32卷 238-253页

作者： Badreddine, Ahmed Ben Romdhane, Taieb Ben HajKacem, Mohamed Aymen Ben Amor, Nahla LARODEC Inst Super Gest Tunis Le Bardo 2000 Tunisia Inst Natl Sci Appl & Technol Ctr Urbain Nord BP 676 Tunis 1080 Tunisia

Bow tie diagram has become a popular method to implement safety barriers. It defines several preventive and protective barriers to reduce respectively the frequency and severity of a given risk. These barriers are often defined by experts that ignore the real aspect of the system. However, the definition of barriers based on experts experiences limits this method because it seems unrealistic to use static recommendations in real dynamic systems. This paper proposes a new multi-objectives approach to implement preventive and protective barriers. The proposed approach is mainly based on three phases namely;a parameters learning phase, a simulation phase and a selection phase. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Bow tie diagram Preventive barriers Protective barriers propagation algorithms Multi-objectives influence diagrams

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Implementing logical connectives in constraint programming

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ARTIFICIAL INTELLIGENCE 2010年第16-17期174卷 1407-1429页

作者： Jefferson, Christopher Moore, Neil C. A. Nightingale, Peter Petrie, Karen E. Univ St Andrews Sch Comp Sci St Andrews KY16 9SX Fife Scotland Univ Dundee Sch Comp Dundee DD1 4HN Scotland

Combining constraints using logical connectives such as disjunction is ubiquitous in constraint programming, because it adds considerable expressive power to a constraint language. We explore the solver architecture needed to propagate such combinations of constraints efficiently. In particular we describe two new features named satisfying sets and constraint trees. We also make use of movable triggers (Gent et al., 2006) [1], and with these three complementary features we are able to make considerable efficiency gains. A key reason for the success of Boolean Satisfiability (SAT) solvers is their ability to propagate OR constraints efficiently, making use of movable triggers. We successfully generalise this approach to an OR of an arbitrary set of constraints, maintaining the crucial property that at most two constraints are active at any time, and no computation at all is done on the others. We also give an AND propagator within our framework, which may be embedded within the OR. Using this approach, we demonstrate speedups of over 10,000 times in some cases, compared to traditional constraint programming approaches. We also prove that the OR algorithm enforces generalised arc consistency (GAC) when all its child constraints have a GAC propagator, and no variables are shared between children. By extending the OR propagator, we present a propagator for ATLEASTK, which expresses that at least k of its child constraints are satisfied in any solution. Some logical expressions (e.g. exclusive-or) cannot be compactly expressed using AND, OR and ATLEASTK. Therefore we investigate reification of constraints. We present a fast generic algorithm for reification using satisfying sets and movable triggers. (C) 2010 Published by Elsevier B.V.

关键词： Constraint programming Constraint satisfaction problems propagation algorithms Logical connectives

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Inference in hybrid Bayesian networks using dynamic discretization

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STATISTICS AND COMPUTING 2007年第3期17卷 219-233页

作者： Neil, Martin Tailor, Manesh Marquez, David Queen Mary Univ London Dept Comp Sci London E1 4NS England Agena Ltd London EC1N 8DL England

We consider approximate inference in hybrid Bayesian Networks (BNs) and present a new iterative algorithm that efficiently combines dynamic discretization with robust propagation algorithms on junction trees. Our approach offers a significant extension to Bayesian Network theory and practice by offering a flexible way of modeling continuous nodes in BNs conditioned on complex configurations of evidence and intermixed with discrete nodes as both parents and children of continuous nodes. Our algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily. The results from the empirical trials clearly show how our software can deal effectively with different type of hybrid models containing elements of expert judgment as well as statistical inference. In particular, we show how the rapid convergence of the algorithm towards zones of high probability density, make robust inference analysis possible even in situations where, due to the lack of information in both prior and data, robust sampling becomes unfeasible.

关键词： Bayesian networks expert systems Bayesian software reasoning under uncertainty statistical inference propagation algorithms dynamic discretization

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Credal Nets with Probabilities Estimated with an Extreme Imprecise Dirichlet Model

Credal Nets with Probabilities Estimated with an Extreme Imp...

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5th International Symposium on Imprecise Probability

作者： Cano, A. Gomez-Olmedo, M. Moral, S. Univ Granada Dpto Ciencias Computac E-18071 Granada Spain

ISBN: (纸本)9788086742205

The propagation of probabilities in credal networks when probabilities are estimated with a global imprecise Dirichlet model is an important open problem. Only Zaffalon [21] has proposed an algorithm for the Naive classifier. The main difficulty is that, in general, computing upper and lower probability intervals implies the resolution of an optimization of a fraction of two polynomials. In the case of the Naive credal classifier, Zaffalon has shown that the function is a convex function of only one parameter, but there is riot a similar result for general credal sets. In this paper, we propose the use of an imprecise global model, but we restrict the distributions to only the most extreme ones. The result is a model giving rise that in the case of estimating a, conditional probability under independence relationships, it can produce smaller intervals than the global general model. Its main advantage is that the optimization problem is simpler, and available procedures can be directly applied, as the ones proposed in [7].

关键词： Locally specified credal networks global imprecise Dirichlet model propagation algorithms probability trees

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