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We present a class of models that, via a simple construction, enables exact, incremental, non-parametric, polynomial-time, Bayesian inference of conditional measures. The approach relies upon creating a sequence of covers on the conditioning variable and m ...
The problem of estimating continuous-domain autoregressive moving-average processes from sampled data is considered. The proposed approach incorporates the sampling process into the problem formulation while introducing exponential models for both the cont ...
We study a new image sensor that is reminiscent of traditional photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. To analyze its performance, we formulate the oversam ...
Large-scale sequencing of genomes has enabled the inference of phylogenies based on the evolution of genomic architecture, under such events as rearrangements, duplications, and losses. Many evolutionary models and associated algorithms have been designed ...
Motivated by the need to smooth and to summarize multiple simultaneous time series arising from networks of environmental monitors, we propose a hierarchical wavelet model for which estimation of hyperparameters can be performed by marginal maximum likelih ...
We report the selective detection of single nitric oxide (NO) mols. using a specific DNA sequence of d(AT)15 oligonucleotides, adsorbed to an array of near-IR fluorescent semiconducting single-walled carbon nanotubes (AT15-SWNT). While SWNT suspended with ...
In this paper, we investigate the construction of compromise estimators of location and scale, by averaging over several models selected among a specified large set of possible models. The weight given to each distribution is based on the profile likelihoo ...
This paper presents a new method for learning overcomplete dictionaries adapted to efficient joint representation of stereo images. We first formulate a sparse stereo image model where the multi-view correlation is described by local geometric transforms o ...
Institute of Electrical and Electronics Engineers2011
We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations. The framework proposes a general objective function suitable for regression, ...
Spectrum sensing is one of the enabling functionalities for cognitive radio systems to operate in the spectrum white space. To protect the primary incumbent users from interference, the cognitive radio is required to detect incumbent signals at very low si ...
