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On Bayesian modeling of fat tails and skewness
 J. Am. Statist. Assoc
, 1998
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Cited by 169 (11 self)
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General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.? Users may download and print one copy of any publication from the public portal for the purpose of private study or research? You may not further distribute the material or use it for any profitmaking activity or commercial gain? You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Using Secure Coprocessors
, 1994
"... The views and conclusions in this document are those of the authors and do not necessarily represent the official policies or endorsements of any of the research sponsors. How do we build distributed systems that are secure? Cryptographic techniques can be used to secure the communications between p ..."
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Cited by 167 (8 self)
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The views and conclusions in this document are those of the authors and do not necessarily represent the official policies or endorsements of any of the research sponsors. How do we build distributed systems that are secure? Cryptographic techniques can be used to secure the communications between physically separated systems, but this is not enough: we must be able to guarantee the privacy of the cryptographic keys and the integrity of the cryptographic functions, in addition to the integrity of the security kernel and access control databases we have on the machines. Physical security is a central assumption upon which secure distributed systems are built; without this foundation even the best cryptosystem or the most secure kernel will crumble. In this thesis, I address the distributed security problem by proposing the addition of a small, physically secure hardware module, a secure coprocessor, to standard workstations and PCs. My central axiom is that secure coprocessors are able to maintain the privacy of the data they process. This thesis attacks the distributed security problem from multiple sides. First, I analyze the security properties of existing system components, both at the hardware and
An exact likelihood analysis of the multinomial probit model
, 1994
"... We develop new methods for conducting a finite sample, likelihoodbased analysis of the multinomial probit model. Using a variant of the Gibbs sampler, an algorithm is developed to draw from the exact posterior of the multinomial probit model with correlated errors. This approach avoids direct evalu ..."
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Cited by 163 (6 self)
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We develop new methods for conducting a finite sample, likelihoodbased analysis of the multinomial probit model. Using a variant of the Gibbs sampler, an algorithm is developed to draw from the exact posterior of the multinomial probit model with correlated errors. This approach avoids direct evaluation of the likelihood and, thus, avoids the problems associated with calculating choice probabilities which affect both the standard likelihood and method of simulated moments approaches. Both simulated and actual consumer panel data are used to fit sixdimensional choice models. We also develop methods for analyzing random coefficient and multiperiod probit models.
Generative models for discovering sparse distributed representations
 Philosophical Transactions of the Royal Society B
, 1997
"... We describe a hierarchical, generative model that can be viewed as a nonlinear generalization of factor analysis and can be implemented in a neural network. The model uses bottomup, topdown and lateral connections to perform Bayesian perceptual inference correctly. Once perceptual inference has b ..."
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Cited by 141 (7 self)
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We describe a hierarchical, generative model that can be viewed as a nonlinear generalization of factor analysis and can be implemented in a neural network. The model uses bottomup, topdown and lateral connections to perform Bayesian perceptual inference correctly. Once perceptual inference has been performed the connection strengths can be updated using a very simple learning rule that only requires locally available information. We demonstrate that the network learns to extract sparse, distributed, hierarchical representations.
MCDB: a Monte Carlo approach to managing uncertain data
, 2008
"... To deal with data uncertainty, existing probabilistic database systems augment tuples with attributelevel or tuplelevel probability values, which are loaded into the database along with the data itself. This approach can severely limit the system’s ability to gracefully handle complex or unforese ..."
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Cited by 108 (3 self)
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To deal with data uncertainty, existing probabilistic database systems augment tuples with attributelevel or tuplelevel probability values, which are loaded into the database along with the data itself. This approach can severely limit the system’s ability to gracefully handle complex or unforeseen types of uncertainty, and does not permit the uncertainty model to be dynamically parameterized according to the current state of the database. We introduce MCDB, a system for managing uncertain data that is based on a Monte Carlo approach. MCDB represents uncertainty via “VG functions,” which are used to pseudorandomly generate realized values for uncertain attributes. VG functions can be parameterized on the results of SQL queries over “parameter tables ” that are stored in the database, facilitating whatif analyses. By storing parameters, and not probabilities, and by estimating, rather than exactly computing, the probability distribution over possible query answers, MCDB avoids many of the limitations of prior systems. For example, MCDB can easily handle arbitrary joint probability distributions over discrete or continuous attributes, arbitrarily complex SQL queries, and arbitrary functionals of the queryresult distribution such as means, variances, and quantiles. To achieve good performance, MCDB uses novel query processing techniques, executing a query plan exactly once, but over “tuple bundles ” instead of ordinary tuples. Experiments indicate that our enhanced functionality can be obtained with acceptable overheads relative to traditional systems.
Boltzmann Samplers For The Random Generation Of Combinatorial Structures
 Combinatorics, Probability and Computing
, 2004
"... This article proposes a surprisingly simple framework for the random generation of combinatorial configurations based on what we call Boltzmann models. The idea is to perform random generation of possibly complex structured objects by placing an appropriate measure spread over the whole of a combina ..."
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Cited by 107 (3 self)
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This article proposes a surprisingly simple framework for the random generation of combinatorial configurations based on what we call Boltzmann models. The idea is to perform random generation of possibly complex structured objects by placing an appropriate measure spread over the whole of a combinatorial class  an object receives a probability essentially proportional to an exponential of its size. As demonstrated here, the resulting algorithms based on realarithmetic operations often operate in linear time. They can be implemented easily, be analysed mathematically with great precision, and, when suitably tuned, tend to be very efficient in practice.
The CrossEntropy Method for Combinatorial and Continuous Optimization
, 1999
"... We present a new and fast method, called the crossentropy method, for finding the optimal solution of combinatorial and continuous nonconvex optimization problems with convex bounded domains. To find the optimal solution we solve a sequence of simple auxiliary smooth optimization problems based on ..."
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Cited by 104 (9 self)
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We present a new and fast method, called the crossentropy method, for finding the optimal solution of combinatorial and continuous nonconvex optimization problems with convex bounded domains. To find the optimal solution we solve a sequence of simple auxiliary smooth optimization problems based on KullbackLeibler crossentropy, importance sampling, Markov chain and Boltzmann distribution. We use importance sampling as an important ingredient for adaptive adjustment of the temperature in the Boltzmann distribution and use KullbackLeibler crossentropy to find the optimal solution. In fact, we use the mode of a unimodal importance sampling distribution, like the mode of beta distribution, as an estimate of the optimal solution for continuous optimization and Markov chains approach for combinatorial optimization. In the later case we show almost surely convergence of our algorithm to the optimal solution. Supporting numerical results for both continuous and combinatorial optimization problems are given as well. Our empirical studies suggest that the crossentropy method has polynomial in the size of the problem running time complexity.
Anytime pointbased approximations for large pomdps
 Journal of Artificial Intelligence Research
, 2006
"... The Partially Observable Markov Decision Process has long been recognized as a rich framework for realworld planning and control problems, especially in robotics. However exact solutions in this framework are typically computationally intractable for all but the smallest problems. A wellknown tech ..."
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Cited by 102 (7 self)
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The Partially Observable Markov Decision Process has long been recognized as a rich framework for realworld planning and control problems, especially in robotics. However exact solutions in this framework are typically computationally intractable for all but the smallest problems. A wellknown technique for speeding up POMDP solving involves performing value backups at specific belief points, rather than over the entire belief simplex. The efficiency of this approach, however, depends greatly on the selection of points. This paper presents a set of novel techniques for selecting informative belief points which work well in practice. The point selection procedure is combined with pointbased value backups to form an effective anytime POMDP algorithm called PointBased Value Iteration (PBVI). The first aim of this paper is to introduce this algorithm and present a theoretical analysis justifying the choice of belief selection technique. The second aim of this paper is to provide a thorough empirical comparison between PBVI and other stateoftheart POMDP methods, in particular the Perseus algorithm, in an effort to highlight their similarities and differences. Evaluation is performed using both standard POMDP domains and realistic robotic tasks.
Everything you always wanted to know about copula modeling but were afraid to ask
 Journal of Hydrologic Engineering
, 2007
"... Abstract: This paper presents an introduction to inference for copula models, based on rank methods. By working out in detail a small, fictitious numerical example, the writers exhibit the various steps involved in investigating the dependence between two random variables and in modeling it using co ..."
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Cited by 89 (1 self)
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Abstract: This paper presents an introduction to inference for copula models, based on rank methods. By working out in detail a small, fictitious numerical example, the writers exhibit the various steps involved in investigating the dependence between two random variables and in modeling it using copulas. Simple graphical tools and numerical techniques are presented for selecting an appropriate model, estimating its parameters, and checking its goodnessoffit. A larger, realistic application of the methodology to hydrological data is then presented.