Ground-state mass in short lattices by controlling overconfidence and bias in Bayesian fits

Gupta S, Lahiri A (2019)
EPL 128(1): 11003.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Gupta, Sourendu; Lahiri, AnirbanUniBi
Abstract / Bemerkung
We investigate the seemingly ill-defined problem of extracting a ground-state mass from a lattice simulation where the extent of the lattice is not long enough to project out the ground-state properly. We regulate the problem using a Bayesian method. We show that controlling meta-parameters (overconfidence) can allow the data to overcome the input priors (bias). We can write the method as a black-box technique which allows extraction of a ground-state mass, even on a relatively short lattice. Copyright (C) EPLA, 2019.
Erscheinungsjahr
2019
Zeitschriftentitel
EPL
Band
128
Ausgabe
1
Art.-Nr.
11003
ISSN
0295-5075
eISSN
1286-4854
Page URI
https://pub.uni-bielefeld.de/record/2942099

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Gupta S, Lahiri A. Ground-state mass in short lattices by controlling overconfidence and bias in Bayesian fits. EPL. 2019;128(1): 11003.
Gupta, S., & Lahiri, A. (2019). Ground-state mass in short lattices by controlling overconfidence and bias in Bayesian fits. EPL, 128(1), 11003. doi:10.1209/0295-5075/128/11003
Gupta, Sourendu, and Lahiri, Anirban. 2019. “Ground-state mass in short lattices by controlling overconfidence and bias in Bayesian fits”. EPL 128 (1): 11003.
Gupta, S., and Lahiri, A. (2019). Ground-state mass in short lattices by controlling overconfidence and bias in Bayesian fits. EPL 128:11003.
Gupta, S., & Lahiri, A., 2019. Ground-state mass in short lattices by controlling overconfidence and bias in Bayesian fits. EPL, 128(1): 11003.
S. Gupta and A. Lahiri, “Ground-state mass in short lattices by controlling overconfidence and bias in Bayesian fits”, EPL, vol. 128, 2019, : 11003.
Gupta, S., Lahiri, A.: Ground-state mass in short lattices by controlling overconfidence and bias in Bayesian fits. EPL. 128, : 11003 (2019).
Gupta, Sourendu, and Lahiri, Anirban. “Ground-state mass in short lattices by controlling overconfidence and bias in Bayesian fits”. EPL 128.1 (2019): 11003.
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