Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

By Ryan P. Adams, Iain Murray, David John Cameron MacKay. 2009

269 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability, Probabilistic & Bayesian Methods

How researchers see this paper

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

In Artificial Intelligence

1. Lawrence Carin
2. Ian David Reid
3. David M. Blei
4. Ricardo Henao
5. Anton Milan
6. Michael A. Osborne
7. Stephen J. Roberts
8. Nuno Vasconcelos

In Statistics and Probability

1. David M. Blei
2. Michael A. Osborne
3. Lawrence Carin
4. Stephen J. Roberts
5. Ricardo Henao
6. Chris M. Lloyd
7. L. Leal-Taix'e
8. Flávio B. Gonçalves

In Probabilistic & Bayesian Methods

1. Lawrence Carin
2. Antoni B. Chan
3. Nuno Vasconcelos
4. David M. Blei
5. Stephen J. Roberts
6. Ricardo Henao
7. Michael A. Osborne
8. Javen Qinfeng Shi

Citations in the corpus

• Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks (2018, methodology): “However, existing approaches often rely on parametric models, e.g. i.i.d. Poisson point or Gaussian Process assumptions Adams et al. (2009); Vo et al. (2016).”
• Counting People With Low-Level Features and Bayesian Regression (2012, background): “[34] propose hierarchical Poisson models, where the log-arrival rate is modeled with a GP prior.”
• Variational Inference for Gaussian Process Modulated Poisson Processes (2014, methodology): “We will use variational inference to obtain a bound on the model evidence p ( D ).”
• Learn to Predict Sets Using Feed-Forward Neural Networks (2020, background): “Poisson point or Gaussian Process assumptions [38], [39].”
• Dirichlet Process Mixtures of Generalized Linear Models (2009, background): “GPs are can model many response types, including continuous, categorical, and count data (Rasmussen and Williams, 2006; Adams et al., 2009).”
• Exact Bayesian inference in spatiotemporal Cox processes driven by multivariate Gaussian processes (2015, methodology): “…about MCMC sampling, Piotr Zwiernik for insightful discussions about matrices, Ryan Adams for providing the MATLAB code to run the algorithm of Adams et al. (2009), Jony A. Pinto, Jr, and Jesus E. Gamboa for their help in the data analyses with the discretized versions of the models and…”
• A Multitask Point Process Predictive Model (2015, methodology): “One thread of work focuses on learning arrival rates by imposing smoothness on a latent rate function (Adams et al., 2009; Rao & Teh, 2011; Lloyd et al., 2014).”