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Results 1 - 10
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34
Lifted Belief Propagation: Pairwise Marginals and Beyond
"... Lifted belief propagation (LBP) can be extremely fast at computing approximate marginal probability distributions over single ground atoms and neighboring ones in the underlying graphical model. It does, however, not prescribe a way to compute joint distributions over pairs, triples or k-tuples of d ..."
Abstract
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Cited by 5 (5 self)
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in each step from scratch, therefore often canceling the benefits of lifted inference. We show how to avoid this by efficiently computing the lifted network for each conditioning directly from the one already known for the single node marginals. Our experimental results validate that significant
Whom You Know Matters: Venture Capital Networks and Investment Performance,
- Journal of Finance
, 2007
"... Abstract Many financial markets are characterized by strong relationships and networks, rather than arm's-length, spot-market transactions. We examine the performance consequences of this organizational choice in the context of relationships established when VCs syndicate portfolio company inv ..."
Abstract
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Cited by 138 (8 self)
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networked VCs may simply be better at hoodwinking the public markets into buying their more marginal companies, but find little support for this explanation. Our main results are based on centrality measures derived from syndication networks that span all industries and the entire United States
From MAP to marginals: Variational inference in Bayesian submodular models
- In Neural Information Processing Systems (NIPS
, 2014
"... Submodular optimization has found many applications in machine learning and beyond. We carry out the first systematic investigation of inference in probabilis-tic models defined through submodular functions, generalizing regular pairwise MRFs and Determinantal Point Processes. In particular, we pres ..."
Abstract
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Cited by 6 (1 self)
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Submodular optimization has found many applications in machine learning and beyond. We carry out the first systematic investigation of inference in probabilis-tic models defined through submodular functions, generalizing regular pairwise MRFs and Determinantal Point Processes. In particular, we
The Space Elevator Feasibility Condition
"... This paper ties together parameters pertaining to tether specific strength and to power system mass density to arrive at an inequality that determines whether a space elevator system is viable. The principle for the feasibility condition (FC) is that a space elevator must be able to lift its own wei ..."
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This paper ties together parameters pertaining to tether specific strength and to power system mass density to arrive at an inequality that determines whether a space elevator system is viable. The principle for the feasibility condition (FC) is that a space elevator must be able to lift its own
A Two-Step Estimation Procedure and a Goodness-of-Fit Test for Spatial Extremes Models
"... Parametric max-stable processes are increasingly used to model spatial extremes. Since the dependence structure is specified for block maxima, the data used for inference are block maxima from all sites. To improve the estimation efficiency, we propose a two-step approach with composite likelihood t ..."
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and quantile-quantile plot. We proposed a goodness-of-fit test for max-stable processes based on the comparison between a nonparametric and a parametric estimator of the corresponding unknown multivariate Pickands dependence function. The proposed two-step procedure separates the estimation of marginal
MODELING EXTREME VALUES OF PROCESSES OBSERVED AT IRREGULAR TIME STEPS: APPLICATION TO
"... This work is motivated by the analysis of the extremal behavior of buoy and satellite data describing wave conditions in the North Atlantic Ocean. The available data sets consist of time series of significant wave height (Hs) with irregular time sampling. In such a situation, the usual statistical m ..."
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This work is motivated by the analysis of the extremal behavior of buoy and satellite data describing wave conditions in the North Atlantic Ocean. The available data sets consist of time series of significant wave height (Hs) with irregular time sampling. In such a situation, the usual statistical
© Institute of Mathematical Statistics, 2012 Statistical Modeling of Spatial Extremes1
"... Abstract. The areal modeling of the extremes of a natural process such as rainfall or temperature is important in environmental statistics; for example, understanding extreme areal rainfall is crucial in flood protection. This ar-ticle reviews recent progress in the statistical modeling of spatial e ..."
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on rain-fall in Switzerland. Whereas latent variable modeling allows a better fit to marginal distributions, it fits the joint distributions of extremes poorly, so appropriately-chosen copula or max-stable models seem essential for suc-cessful spatial modeling of extremes.
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