Results 251 to 260 of about 1,703,012 (264)

Distributed predictive subspace pursuit

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
In a compressed sensing setup with jointly sparse, correlated data, we develop a distributed greedy algorithm called distributed predictive subspace pursuit. Based on estimates from neighboring sensor nodes, this algorithm operates iteratively in two steps: first forming a prediction of the signal and then solving the compressed sensing problem with an
Dennis Sundman, Dave Zachariah, Saikat Chatterjee, Mikael Skoglund   +3 more
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Distributed model predictive control

Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001
We explore a distributed model predictive control (DMPC) scheme. The controllers apply model predictive control (MPC) policies to their local subsystems. They exchange their predictions by communication and incorporate the information from other controllers into their local MPC problem so as to coordinate with each other.
null Dong Jia, B.H. Krogh
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Bayes beyond the predictive distribution

Behavioral and Brain Sciences
Abstract Binz et al. argue that meta-learned models offer a new paradigm to study human cognition. Meta-learned models are proposed as alternatives to Bayesian models based on their capability to learn identical posterior predictive distributions.
Anna Székely, Gergő Orbán
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Predicting Prime Distribution

Abstract We present PHL‑v3 (Prime Harmonic Locator, version 3), a rigorously‑specified, polynomial‑time procedure that predicts prime locations with empirical accuracy exceeding 99·9999 % on x≤1011x\le10^{11}, no brute force required. Building on the Phase‑Crest/Curvature model of the Prime Skeleton Key and the self‑adjoint framework of the Prime ...
ASHER, KIMBERLEY LAVERNE, ASHER, ANESKA
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Statistical Predictive Distributions

1981
As their name implies predictive distributions are designed primarily to play a predictive role in statistical applications but they also have a role in a number of other applications, particularly when modelling is of a complex nature. The purpose of this paper is to review the state of the art, first recalling the basic structure of the prediction ...
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Posterior Predictive Distribution

2015
The posterior predictive distribution is the distribution of future observations, conditioned on the information available from existing observations. It is the main Bayesian tool for treating predictive problems in statistics. We define the posterior predictive distribution and illustrate its main features in Bayesian parametric inference.
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Predicting Tree Diameter Distributions

2013
Diameter distribution of trees is an important stand attribute that describes stand structure in terms of volume, biomass, value, growth and biodiversity factors. Diameter distribution can be characterized using different approaches such as probability density functions, percentile-based distributions or nearest neighbour applications.
Matti Maltamo, Terje Gobakken
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Predicting Corporate Distributions

SSRN Electronic Journal, 2013
Hendrik (Hank) Bessembinder, Feng Zhang
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Predictive distributions

1975
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Bayesian predictive posterior distributions

Bayesian uncertainty can be characterized in a number of ways, the usual one starting with a prior distribution which represents prior uncertainty as to the value of a parameter. This gets updated to the posterior quantification of uncertainty via the data evidence using the likelihood function. This framework is however difficult to relax.
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