BASS

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.

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References in zbMATH (referenced in 8 articles , 1 standard article )

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  1. Manjunath, Vikram: When too little is as good as nothing at all: rationing a disposable good among satiable people with acceptance thresholds (2012)
  2. Holliday, James R.; Turcotte, Donald L.; Rundle, John B.: A review of earthquake statistics: fault and seismicity-based models, ETAS and BASS (2008)
  3. Fruchter, G. E.; Rao, R. C.; Shi, M.: Dynamic network-based discriminatory pricing (2006)
  4. Pham, T. D.: Perception-based hidden Markov models: A theoretical framework for data mining and knowledge discovery (2002)
  5. -: Biopharmaceutical applied statistics. 3rd annual symposium, BASS-3, San Diego, CA, USA, December 1--4, 1996 (1997)
  6. -: Biopharmaceutical applied statistics symposium ’95. 2nd annual symposium, BASS-2. San Diego, CA, USA, December 4--12, 1995 (1996)
  7. Arjas, E.; Mannila, H.; Salmenkivi, M.; Suramo, R.; Toivonen, H.: BASS: Bayesian analyzer of event sequences (1996)
  8. 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)