Bayesian mixture modeling for spatial Poisson process intensities, with applications to extreme value analysis

By Athanasios Kottas, Bruno Sansó. 2007

121 citations recorded by the source corpus.

Subfields: Applied Mathematics, Artificial Intelligence, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/8317df91706b148e30dac259c964491c0b1ce7c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

1. Matt Taddy
2. Alan E. Gelfand
3. Thomas J. Leininger
4. Flávio B. Gonçalves
5. Dani Gamerman
6. Iain Murray
7. David John Cameron MacKay
8. Ryan P. Adams

In Artificial Intelligence

1. Ryan P. Adams
2. Matt Taddy
3. Alan E. Gelfand
4. Athanasios Kottas
5. Iain Murray
6. Thomas J. Leininger
7. Dani Gamerman
8. Flávio B. Gonçalves

In Economics and Econometrics

1. Matt Taddy
2. Flávio B. Gonçalves
3. Dani Gamerman
4. Iain Murray
5. David John Cameron MacKay
6. Ryan P. Adams
7. Alan E. Gelfand
8. Athanasios Kottas

Citations in the corpus

• Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities (2009, methodology): “The Dirichlet process mixture of Beta distributions (Kottas & Sans´o, 2007) has the appealing characteristic that it allows tractable nonparametric Bayesian inference via relatively standard MCMC methods.”
• Mixture Modeling for Marked Poisson Processes (2010, methodology): “A possible two-dimensional extension of (3) is that of Kottas and Sansó (2007), which employs a Sarmanov dependence factor to induce a bounded bivariate density with beta marginals.”
• Exact Bayesian inference in spatiotemporal Cox processes driven by multivariate Gaussian processes (2015, methodology): “Another non-parametric exact approach was adopted in Kottas and Sansó (2007), where a particular factorization of the IF was proposed and Dirichlet processes priors were used.”
• Bayesian Inference and Model Assessment for Spatial Point Patterns Using Posterior Predictive Samples (2017, background): “See, e.g., Kottas and Sansó (2007) and Taddy and Kottas (2012).”
• Autoregressive Mixture Models for Dynamic Spatial Poisson Processes: Application to Tracking Intensity of Violent Crime (2010, methodology): “Much of the earlier work is derived from a report by MacEachern (2000), which introduced and outlined the Dependent Dirichlet Process (DDP). This general model formulation holds that G = {Gs1, . . . ,GsR}, the set of random distributions corresponding to points {sr}r=1 ∈ S and collectively distribut”