Intentional sampling by goal optimization with decoupling by stochastic perturbation: why (not) to randomize? (2016)
- Authors:
- USP affiliated authors: STERN, JULIO MICHAEL - IME ; LAURETTO, MARCELO DE SOUZA - EACH ; NAKANO, FÁBIO - EACH ; PEREIRA, CARLOS ALBERTO DE BRAGANCA - IME
- Unidades: IME; EACH
- Subjects: ESTATÍSTICA DE PROCESSOS ESTOCÁSTICOS; ESTATÍSTICA APLICADA
- Language: Inglês
- Imprenta:
- Publisher: Associação Brasileira de Estatística - ABE
- Publisher place: São Paulo
- Date published: 2016
- Source:
- Título do periódico: Book of abstracts
- Volume/Número/Paginação/Ano: p. 17-18
- Conference titles: Latin American Conference on Statistical Computing - LACSC
-
ABNT
STERN, Julio Michael et al. Intentional sampling by goal optimization with decoupling by stochastic perturbation: why (not) to randomize? 2016, Anais.. São Paulo: Associação Brasileira de Estatística - ABE, 2016. p. 17-18. Disponível em: https://www.ime.usp.br/~lsanchez/lacsc2016/bookabstracts_complete.pdf. Acesso em: 19 abr. 2024. -
APA
Stern, J. M., Lauretto, M. de S., Nakano, F., & Pereira, C. A. de B. (2016). Intentional sampling by goal optimization with decoupling by stochastic perturbation: why (not) to randomize? In Book of abstracts (p. 17-18). São Paulo: Associação Brasileira de Estatística - ABE. Recuperado de https://www.ime.usp.br/~lsanchez/lacsc2016/bookabstracts_complete.pdf -
NLM
Stern JM, Lauretto M de S, Nakano F, Pereira CA de B. Intentional sampling by goal optimization with decoupling by stochastic perturbation: why (not) to randomize? [Internet]. Book of abstracts. 2016 ; 17-18.[citado 2024 abr. 19 ] Available from: https://www.ime.usp.br/~lsanchez/lacsc2016/bookabstracts_complete.pdf -
Vancouver
Stern JM, Lauretto M de S, Nakano F, Pereira CA de B. Intentional sampling by goal optimization with decoupling by stochastic perturbation: why (not) to randomize? [Internet]. Book of abstracts. 2016 ; 17-18.[citado 2024 abr. 19 ] Available from: https://www.ime.usp.br/~lsanchez/lacsc2016/bookabstracts_complete.pdf - Hierarchical forecasting with polynomial nets
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