Modeling count data from multiple sensors: A building occupancy model

2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP

Volumn , Issue , 2007, Pages 241-244

  Hutchins, Jon ^a   Ihler, Alexander ^a   Smyth, Padhraic ^a  

^a Irvine   (United States)

Author keywords

Bayesian inference; Graphical models; Occupancy models; Sensor networks

Indexed keywords

ADAPTIVE PROCESSING; BAYESIAN INFERENCE; BUILDING OCCUPANCY; CORRUPTED DATA; EMERGENCY RESPONSES; GRAPHICAL MODELS; MULTI SENSORS; MULTIPLE SENSORS; NOISY MEASUREMENTS; OCCUPANCY MODELS; OPTICAL COUNTING; PROBABILISTIC MODEL; REAL DATA; SENSOR NOISES;

BUILDINGS; FORECASTING; OPTICAL SENSORS; SENSORS;

SENSOR NETWORKS;

EID: 50249154825     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CAMSAP.2007.4498010     Document Type: Conference Paper

References (6)

1. Freeway Performance Measurement System (PeMS), "http: //pems.eecs.berkeley.edu/," .
2. A. Ihler, J. Hutchins, and P. Smyth, "Adaptive event detection with time-varying Poisson processes," in ACM Int'l Conf. Knowledge Discovery and Data mining, 2006, pp. 207-216.
3. C.R. Wren, D.C. Minnen, and S.G. Rao, "Similarity-based analysis for large networks of ultra-low resolution sensors," Pattern Recognition, vol. 39, no. 10, Oct. 2006.
4. S.Scott, Bayesian Methods and Extensions for the Two State Markov Modulated Poisson Process, Ph.D. thesis, Harvard University, 1998.
5. S. Scott and P. Smyth, "The Markov modulated Poisson process and Markov Poisson cascade with applications to web traffic data," Bayesian Statistics, vol. 7, pp. 671-680, 2003.
6. P. Felzenszwalb and D. Huttenlocher, "Belief propagation for early vision," Int'l J. of Comp. Vision, vol. 70, no. 1, Oct. 2006.