Source: International Journal of Approximate Reasoning. Unidade: IME
Subjects: INFERÊNCIA BAYESIANA, INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL
ABNT
LASSANCE, Rodrigo Ferrari Lucas e IZBICKI, Rafael e STERN, Rafael Bassi. Adding imprecision to hypotheses: a Bayesian framework for testing practical significance in nonparametric settings. International Journal of Approximate Reasoning, v. 178, n. artigo 109332, p. 1-25, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2024.109332. Acesso em: 21 maio 2025.APA
Lassance, R. F. L., Izbicki, R., & Stern, R. B. (2025). Adding imprecision to hypotheses: a Bayesian framework for testing practical significance in nonparametric settings. International Journal of Approximate Reasoning, 178( artigo 109332), 1-25. doi:10.1016/j.ijar.2024.109332NLM
Lassance RFL, Izbicki R, Stern RB. Adding imprecision to hypotheses: a Bayesian framework for testing practical significance in nonparametric settings [Internet]. International Journal of Approximate Reasoning. 2025 ; 178( artigo 109332): 1-25.[citado 2025 maio 21 ] Available from: https://doi.org/10.1016/j.ijar.2024.109332Vancouver
Lassance RFL, Izbicki R, Stern RB. Adding imprecision to hypotheses: a Bayesian framework for testing practical significance in nonparametric settings [Internet]. International Journal of Approximate Reasoning. 2025 ; 178( artigo 109332): 1-25.[citado 2025 maio 21 ] Available from: https://doi.org/10.1016/j.ijar.2024.109332