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An Introduction To The Theory Of Point Processes
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Citations
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Journal ArticleDOI
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter
Ba-Ngu Vo,Ba-Tuong Vo,Dinh Phung +2 more
TL;DR: The present paper details efficient implementations of the δ-GLMB multi-target tracking filter and presents inexpensive look-ahead strategies to reduce the number of computations.
Proceedings ArticleDOI
Recurrent Marked Temporal Point Processes: Embedding Event History to Vector
TL;DR: The Recurrent Marked Temporal Point Process is proposed to simultaneously model the event timings and the markers, and uses a recurrent neural network to automatically learn a representation of influences from the event history, and an efficient stochastic gradient algorithm is developed for learning the model parameters.
Journal ArticleDOI
Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial
TL;DR: In this article, a tutorial on stochastic geometry-based analysis for cellular networks is presented, which is distinguished by its depth with respect to wireless communication details and its focus on cellular networks.
Journal ArticleDOI
Terrestrial Planet Occurrence Rates for the Kepler GK Dwarf Sample
Christopher J. Burke,Jessie L. Christiansen,Fergal Mullally,Shawn Seader,Daniel Huber,Daniel Huber,Daniel Huber,Jason F. Rowe,Jeffrey L. Coughlin,Susan E. Thompson,Joseph Catanzarite,Bruce D. Clarke,Timothy D. Morton,Douglas A. Caldwell,Stephen T. Bryson,Michael R. Haas,Natalie M. Batalha,Jon M. Jenkins,Peter Tenenbaum,Joseph D. Twicken,Jie Li,Elisa V. Quintana,Thomas Barclay,Christopher E. Henze,William J. Borucki,Steve B. Howell,Martin Still +26 more
TL;DR: In this article, the authors examined the Kepler GK dwarf target sample for planet radii, 0.75
Posted Content
Discovering Latent Network Structure in Point Process Data
Scott W. Linderman,Ryan P. Adams +1 more
TL;DR: In this article, a probabilistic model that combines mutually-exciting point processes with random graph models is developed to enable analysis of implicit networks, which enables an elegant auxiliary variable formulation and a fully-Bayesian, parallel inference algorithm.
References
More filters
Journal ArticleDOI
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter
Ba-Ngu Vo,Ba-Tuong Vo,Dinh Phung +2 more
TL;DR: The present paper details efficient implementations of the δ-GLMB multi-target tracking filter and presents inexpensive look-ahead strategies to reduce the number of computations.
Proceedings ArticleDOI
Recurrent Marked Temporal Point Processes: Embedding Event History to Vector
TL;DR: The Recurrent Marked Temporal Point Process is proposed to simultaneously model the event timings and the markers, and uses a recurrent neural network to automatically learn a representation of influences from the event history, and an efficient stochastic gradient algorithm is developed for learning the model parameters.
Journal ArticleDOI
Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial
TL;DR: In this article, a tutorial on stochastic geometry-based analysis for cellular networks is presented, which is distinguished by its depth with respect to wireless communication details and its focus on cellular networks.
Journal ArticleDOI
Terrestrial Planet Occurrence Rates for the Kepler GK Dwarf Sample
Christopher J. Burke,Jessie L. Christiansen,Fergal Mullally,Shawn Seader,Daniel Huber,Daniel Huber,Daniel Huber,Jason F. Rowe,Jeffrey L. Coughlin,Susan E. Thompson,Joseph Catanzarite,Bruce D. Clarke,Timothy D. Morton,Douglas A. Caldwell,Stephen T. Bryson,Michael R. Haas,Natalie M. Batalha,Jon M. Jenkins,Peter Tenenbaum,Joseph D. Twicken,Jie Li,Elisa V. Quintana,Thomas Barclay,Christopher E. Henze,William J. Borucki,Steve B. Howell,Martin Still +26 more
TL;DR: In this paper, the authors examined the Kepler GK dwarf target sample for planet radii and orbital periods, with an emphasis on a thorough exploration and identification of the most important sources of systematic uncertainties.
Book
The Surprising Mathematics of Longest Increasing Subsequences
TL;DR: In a surprising sequence of developments, the longest increasing subsequence problem has proven to have deep connections to many seemingly unrelated branches of mathematics, such as random permutations, random matrices, Young tableaux, and the corner growth model as discussed by the authors.