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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...
Asymptotically optimal parameter estimation under quantization constraints
Asymptotically quantization constraints parameter estimation
2011/3/18
The problem of decentralized parameter estimation is considered for diffusion-type processes whose drift coefficients are linear with respect to the unknown parameter. This problem is motivated by app...
Optimal rates and adaptation in the single-index model using aggregation
Nonparametric regression single-index aggregation adaptation minimax random design oracle inequality
2009/9/16
We want to recover the regression function in the single-index model. Using an aggregation algorithm with local polynomial estimators, we answer in particular to the second part of Question 2 from Sto...
Optimal properties of some Bayesian inferences
relative surprise inferences probability of covering a false value unbiasedness Bayes factors relative belief ratios
2009/9/16
Relative surprise regions are shown to minimize, among Bayesian credible regions, the prior probability of covering a false value from the prior. Such regions are also shown to be unbiased in the sens...
Optimal weighting for false discovery rate control
False discovery rate multiple testing p-value weighting power maximization
2009/9/16
How to weigh the Benjamini-Hochberg procedure? In the context of multiple hypothesis testing, we propose a new step-wise procedure that controls the false discovery rate (FDR) and we prove it to be mo...