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Toward Optimal Stratification for Stratified Monte-Carlo Integration
Toward Optimal Stratification Stratified Monte-Carlo Integration
2013/4/27
We consider the problem of adaptive stratified sampling for Monte Carlo integration of a noisy function, given a finite budget n of noisy evaluations to the function. We tackle in this paper the probl...
Optimal design for linear models with correlated observations
Optimal design correlated observations integral operator,eigenfunctions arcsine distribution logarithmic potential
2013/4/27
In the common linear regression model the problem of determining optimal designs for least squares estimation is considered in the case where the observations are correlated. A necessary condition for...
Universally optimal crossover designs under subject dropout
Crossover designs efficiency robustness subject dropout uni-versal optimality
2013/4/27
Subject dropout is very common in practical applications of crossover designs. However, there is very limited design literature taking this into account. Optimality results have not yet been well esta...
On asymptotically optimal confidence regions and tests for high-dimensional models
asymptotically optimal confidence regions tests for high-dimensional models
2013/4/27
We propose a general method for constructing confidence intervals and statistical tests for single or low-dimensional components of a large parameter vector in a high-dimensional model. It can be easi...
Further Optimal Regret Bounds for Thompson Sampling
Further Optimal Regret Bounds Thompson Sampling
2012/11/23
Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized algorithm based on Bayesian ideas, and has recently generated significant interest after several s...
Dependent Dirichlet Priors and Optimal Linear Estimators for Belief Net Parameters
Dependent Dirichlet Priors Optimal Linear Estimators Belief Net Parameters
2012/9/19
A Bayesian belief network is a model of a joint distribution over a finite set of vari-ables, with a DAG structure representing im-mediate dependencies among the variables.For each node, a table of pa...
Near-Optimal Node Blacklisting in Adversarial Networks
Near-Optimal Node Blacklisting Adversarial Networks
2012/9/18
Many communication networks contain nodes which may misbehave, thus incurring a cost to the network operator. Weconsider the problem of how to manage the nodes when the operator receives a payoff for ...
How to sample if you must: on optimal functional sampling
Learning Theory Other Applications.
2012/9/17
We examine a fundamental problem that models various active sampling setups, such as network tomography. We analyze sampling of a multivariate normal distribution with an unknown expectation that need...
Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions
Stochastic optimization sparse statistical recovery optimal algorithm high dimensions
2012/9/19
We develop and analyze stochastic optimization algorithms for problems in which the ex-pected loss is strongly convex, and the optimum is (approximately)sparse. Previous approaches are able to exploit...
Asymptotic normality of the optimal solution in multiresponse surface methodology
Asymptotic normality multiresponse surface optimisation sensitivity analysis mathematical programming
2012/9/19
In this work is obtained an explicit form for the perturbation effect on the matrix of regression coefficients on the optimal solution in multiresponse surface methodology. Then, the sensitivity analy...
Near-Optimal Algorithms for Differentially-Private Principal Components
Near-Optimal Algorithms Differentially-Private Principal Components
2012/9/19
Principal components analysis (PCA) is a standard tool for identifying good low-dimensional approximations to data sets in high dimension. Many current data sets of interest contain private or sensiti...
Optimal inferential models for a Poisson mean
Belief function constraint plausibility function predic-tive random set recursive ordering score function validity.
2012/9/18
Statistical inference on the mean of a Poisson distribution is a fundamentally important problem with modern applications in, e.g., particle physics. The dis-creteness of the Poisson distribution make...
Optimal Rate Scheduling via Utility-Maximization for J-User MIMO Markov Fading Wireless Channels with Cooperation
Processor-SharingQueues Random Environment Multi-Input Multi-Output
2011/7/7
We design a dynamic rate scheduling policy of Markov type via the solution (a social optimal Nash equilibrium point) to a utility-maximization problem over a randomly evolving capacity set for a class...
Efficient Optimal Learning for Contextual Bandits
Efficient Optimal Learning Contextual Bandits
2011/7/6
We address the problem of learning in an online setting where the learner repeatedly observes features, selects among a set of actions, and receives reward for the action taken.
Sequential Monte Carlo (SMC) approaches have become work horses in approximate Bayesian computation (ABC). Here we discuss how to construct the perturbation kernels that are required in ABC-SMC approa...