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Complexity of Non-Adaptive Optimization Algorithms for a Class of Diffusions
Global optimization average-case complexity diffusion processes
2015/7/8
This paper is concerned with the analysis of the average error in approximating the global minimum of a 1-dimensional, time-homogeneous diffusion by non-adaptive methods. We derive the limiting distri...
On a class of spatial discretizations of equations of the nonlinear Schrödinger type
Discrete nonlinear schrodinger equation nonlinear discrete model and discrete
2014/12/25
We demonstrate the systematic derivation of a class of discretizations of nonlinear Schrödinger (NLS) equations for general polynomial nonlinearity whose stationary solutions can be found from a ...
A delimitation of the support of optimal designs for Kiefer's $φ_p$-class of criteria
Approximate design optimum design support points design algorithm MSC62K05 90C46
2013/4/28
The paper extends the result of Harman and Pronzato [Stat. & Prob. Lett., 77:90--94, 2007], which corresponds to $p=0$, to all strictly concave criteria in Kiefer's $\phi_p$-class. Let $\xi$ be any de...
On a class of space-time intrinsic random functions
Fox’sH-function generalized covariance function Mat′ern covariance function
2013/4/28
Power law generalized covariance functions provide a simple model for describing the local behavior of an isotropic random field. This work seeks to extend this class of covariance functions to spatia...
Concepts and a case study for a flexible class of graphical Markov models
Concepts a case study a flexible class graphical Markov models
2013/4/27
With graphical Markov models, one can investigate complex dependences, summarize some results of statistical analyses with graphs and use these graphs to understand implications of well-fitting models...
Statistically adaptive learning for a general class of cost functions (SA L-BFGS)
Statistically adaptive learning general class cost functions
2012/11/23
We present a system that enables rapid model experimentation for tera-scale machine learning with trillions of non-zero features, billions of training examples, and millions of parameters. Our contrib...
Multi-Instance Learning with Any Hypothesis Class
Multiple-instance learning learning theory sample complexity PAC learning
2011/7/19
In the supervised learning setting termed Multiple-Instance Learning (MIL), the examples are bags of instances, and the bag label is a function of the labels of its instances. Typically, this function...
Exact recording of Metropolis-Hastings-class Monte Carlo simulations using one bit per sample
Markov chain Monte Carlo Metropolis-Hastings information theory data representation
2011/6/21
The Metropolis-Hastings (MH) algorithm is the prototype for a class of Markov chain Monte Carlo methods
that propose transitions between states and then accept or reject the proposal. These methods g...
A New Class of Backward Stochastic Partial Differential Equations with Jumps and Applications
Backward Stochastic Partial Differential Equations with Jumps High-Order Partial Differential Operator Vector Partial Differential Equation Existence and Uniqueness Random Environment
2011/6/21
We formulate a new class of stochastic partial differential equations (SPDEs), named
high-order vector backward SPDEs (B-SPDEs) with jumps, which allow the high-order
integral-partial differential o...
Identification and well-posedness in a class of nonparametric problems
Identification well-posedness nonparametric problems
2010/10/19
This is a companion note to Zinde-Walsh (2010), arXiv:1009.4217v1[MATH.ST], to clarify and extend results on identification in a number of problems that lead to a system of convolution equations. Exam...
On stochastic equations for the class of Gaussian processes
stochastic equations the class of Gaussian processes
2009/9/24
Ito stochastic equations are derived for a class of
multidimensional Gaussian processes appearing in connection with
generalized spline functions. Some analytic consequences for the
spline interpol...
On a Taylor formula for a class of Ito processes
a Taylor formula a class of Ito processes
2009/9/24
In the paper a stochastic generalization of the Taylor
formula for It6 processes of diffusion type is investigated with respect
to mean square, almost sure and weak convergence.
On a class of Banach space valued processes with propagators
a class of Banach space processes with propagators
2009/9/24
In this paper we present a characterization uf vector
stochastic processes with normal propagators and apply it to study
the regularity and singularity of vector stochastic processes.
Uniqueness and extremality for a class of multiply-stochastic measures
Uniqueness extremality a class of multiply-stochastic measures
2009/9/24
Uniqueness and extremality for a class of multiply-stochastic measures。
Modal Clustering in a Class of Product Partition Models
Bayesian nonparametrics Dirichlet process mixture model maximum a posteriori clustering maximum likelihood clustering
2009/9/24
This paper defines a class of univariate product partition models for
which a novel deterministic search algorithm is guaranteed to find the maximum
aposteriori (MAP)clustering or the maximum likeli...