Statistical model selection for stochastic systems with applications to bioinformatics, linguistics and neurobiology (2022)
- Authors:
- USP affiliated authors: GALVES, JEFFERSON ANTONIO - IME ; LEONARDI, FLORENCIA GRACIELA - IME
- Unidade: IME
- Assunto: ESTATÍSTICA APLICADA
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: Impa
- Publisher place: Rio de Janeiro
- Date published: 2022
- Descrição física: 148 p
- ISBN: 9786589124290
- Conference titles: Colóquio Brasileiro de Matemática
-
ABNT
GALVES, Antonio e LEONARDI, Florencia Graciela e OST, Guilherme. Statistical model selection for stochastic systems with applications to bioinformatics, linguistics and neurobiology. . Rio de Janeiro: Impa. Disponível em: https://impa.br/wp-content/uploads/2022/01/33CBM15-eBook.pdf. Acesso em: 15 mar. 2026. , 2022 -
APA
Galves, A., Leonardi, F. G., & Ost, G. (2022). Statistical model selection for stochastic systems with applications to bioinformatics, linguistics and neurobiology. Rio de Janeiro: Impa. Recuperado de https://impa.br/wp-content/uploads/2022/01/33CBM15-eBook.pdf -
NLM
Galves A, Leonardi FG, Ost G. Statistical model selection for stochastic systems with applications to bioinformatics, linguistics and neurobiology [Internet]. 2022 ;[citado 2026 mar. 15 ] Available from: https://impa.br/wp-content/uploads/2022/01/33CBM15-eBook.pdf -
Vancouver
Galves A, Leonardi FG, Ost G. Statistical model selection for stochastic systems with applications to bioinformatics, linguistics and neurobiology [Internet]. 2022 ;[citado 2026 mar. 15 ] Available from: https://impa.br/wp-content/uploads/2022/01/33CBM15-eBook.pdf - Exponential inequalities for empirical unbounded context trees
- Random perturbations of stochastic processes with unbounded variable length memory
- Context tree selection and linguistic rhythm retrieval from written texts
- Sequence motif identification and protein family classification using probabilistic trees
- Consistent model selection for the degree corrected stochastic blockmodel
- Change point detection for high-dimensional regression data with l1-regularization
- Context tree selection: a unifying view
- Some upper bounds for the rate of convergence of penalized likelihood context tree estimators
- Computationally efficient change point detection for high-dimensional regression
- Nonparametric statistical inference for the context tree of a stationary ergodic process
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 |
3124793.pdf | Direct link |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
