Learning time-intensity profiles of human activity using non-parametric Bayesian models

Advances in Neural Information Processing Systems

Volumn , Issue , 2007, Pages 625-632

  Ihler, Alexander T ^a   Smyth, Padhraic ^a  

^a Ringgold Standard Institution Informatics   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION AREA; BASIS SETS; DATA SETS; DIRICHLET PROCESS; HUMAN ACTIVITIES; INTENSITY FUNCTIONS; NON-PARAMETRIC BAYESIAN; REAL WORLD DATA; SHARING INFORMATION; TIME-PERIODS; VIDEO SURVEILLANCE; WEEKEND EFFECTS;

SECURITY SYSTEMS; VIRTUAL REALITY;

BAYESIAN NETWORKS;

EID: 80054843982     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. S. Scott and P. Smyth. The Markov modulated Poisson process and Markov Poisson cascade with applications to web traffic data. Bayesian Statistics, 7:671-680, 2003.
2. R. Helmers, I.W. Mangku, and R. Zitikis. Consistent estimation of the intensity function of a cyclic Poisson process. J. Multivar. Anal., 84(1):19-39, January 2003.
3. R. Willett and R. Nowak. Multiscale Poisson intensity and density estimation. submitted to IEEE Trans. IT, January 2005.
4. A. Kottas. Bayesian nonparametric mixture modeling for the intensity function of non-homogeneous Poisson processes. Technical Report ams2005-02, Department of Applied Math and Statistics, U.C. Santa Cruz, Santa Cruz, CA, 2005.
5. A. Kottas and B. Sanso. Bayesian mixture modeling for spatial Poisson process intensities, with applications to extreme value analysis. Technical Report ams2005-19, Dept. of Applied Math and Statistics, U.C. Santa Cruz, Santa Cruz, CA, 2005.
6. B.W. Silverman. Density Estimation for Statistics and Data Analysis. Chapman & Hall, NY, 1986.
7. Y.W. Teh, M.I. Jordan, M.J. Beal, and D.M. Blei. Hierarchical Dirichlet processes. In NIPS 17, 2004.
8. D.R. Cox. Some statistical methods connected with series of events. J. R. Stat. Soc. B, 17:129-164, 1955.
9. R.M. Neal. Markov chain sampling methods for Dirichlet process mixture models. J. of Comp. Graph. Stat., 9:283-297, 2000.
10. M.D. Escobar and M. West. Bayesian density estimation and inference using mixtures. J. Amer. Stat. Assoc., 90:577-588, 1995.
11. L.F. James. Functionals of Dirichlet processes, the Cifarelli-Reganzzini identity and Beta-Gamma processes. Ann. Stat., 33(2):647-660, 2005.
12. H. Ishwaran and L.F. James. Gibbs sampling methods for stick-breaking priors. J. Amer. Stat. Assoc., 96:161-173, 2001.
13. H. Ishwaran and L.F. James. Approximate Dirichlet process computing in finite normal mixtures: smoothing and prior information. J. Comp. Graph. Statist., 11:508-532, 2002.
14. J. Sethuraman. A constructive definition of Dirichlet priors. Statistica Sinica, 4:639-650, 1994.