Buscar
Mostrando ítems 1-10 de 4824
Objective and subjective prior distributions for the gompertz distribution
(2018-07-01)
This paper takes into account the estimation for the unknown parameters of the Gompertz distribution from the frequentist and Bayesian view points by using both objective and subjective prior distributions. We first derive ...
Particle filter with unknown noise statistics and with prior knowledge
(Asociación Argentina de Control Automático, 2018)
Particle filters have been widely used as a solution to the Bayesian filtering problem, propagating in time a Monte Carlo (MC) approximation of the a posteriori filtering measure. In many situations, the exact statistics ...
Comprehension of topic shifts by Argentinean college students: Role of discourse marker presence, causal connectivity and prior knowledge
(Springer, 2020-06)
The aim of this study was to examine the effect of the presence of the discourse marker "then" ("después" in Spanish), the causal connectivity of the statements, and the prior knowledge of the comprehender on the identification ...
Elicitation of multivariate prior distributions: A nonparametric Bayesian approach
(Elsevier B.V., 2010-07-01)
In the context of Bayesian statistical analysis, elicitation is the process of formulating a prior density f(.) about one or more uncertain quantities to represent a person's knowledge and beliefs. Several different methods ...
Elicitation of multivariate prior distributions: A nonparametric Bayesian approach
(Elsevier B.V., 2010-07-01)
In the context of Bayesian statistical analysis, elicitation is the process of formulating a prior density f(.) about one or more uncertain quantities to represent a person's knowledge and beliefs. Several different methods ...
Non-informative priors in GUM Supplement 1
(ELSEVIER SCI LTD, 2011)
Supplement 1 to the 'Guide to the Expression of Uncertainty in Measurement' (GUM S1) proposes a Monte Carlo method for the propagation of the probability density functions (PDFs) assigned to the input quantities that are ...