We describe the BASS system, a Bayesian analyzer of event sequences. BASS uses Markov chain Monte Carlo methods, especially MetropolisHastings algorithm, for exploring posterior distributions. The system allows the user to specify an intensity model in a highlevel definition language, and then runs the MetropolisHastings algorithm on it.
Keywords for this software
References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
- Manjunath, Vikram: When too little is as good as nothing at all: rationing a disposable good among satiable people with acceptance thresholds (2012)
- Holliday, James R.; Turcotte, Donald L.; Rundle, John B.: A review of earthquake statistics: fault and seismicity-based models, ETAS and BASS (2008)
- Fruchter, G. E.; Rao, R. C.; Shi, M.: Dynamic network-based discriminatory pricing (2006)
- Pham, T. D.: Perception-based hidden Markov models: A theoretical framework for data mining and knowledge discovery (2002)
- -: Biopharmaceutical applied statistics. 3rd annual symposium, BASS-3, San Diego, CA, USA, December 1--4, 1996 (1997)
- -: Biopharmaceutical applied statistics symposium ’95. 2nd annual symposium, BASS-2. San Diego, CA, USA, December 4--12, 1995 (1996)
- Arjas, E.; Mannila, H.; Salmenkivi, M.; Suramo, R.; Toivonen, H.: BASS: Bayesian analyzer of event sequences (1996)
- Li, Ping’an; Yu, Bianzhang; Sun, Jincai: 2-D spatial-spectrum estimation for wide-band signals without a priori knowledge of the number of sources (1996)