2010
DOI: 10.1590/s1516-35982010000200027
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Bayesian analysis for comparison of nonlinear regression model parameters: an application to ruminal degradability data

Abstract: -This paper shows the Bayesian approach as an alternative to the classical analysis of nonlinear models for ruminal degradation data. The data set was obtained from a Latin square experimental design, established for testing the ruminal degradation of dry matter, crude protein and fiber in neutral detergent of three silages: elephant grass (Pennisetum purpureum Schum) with bacterial inoculant or enzyme-bacterial inoculant and corn silage (Zea mays L.). The incubation times were 0, 2, 6, 12, 24, 48, 72 and 96 h… Show more

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Cited by 6 publications
(9 citation statements)
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“…The modeling follows the suggestion of a Bayesian approach (ROSSI et al, 2010;ROSSI, 2011), t is the incubation time in the rumen, in hours.…”
Section: Methodsmentioning
confidence: 99%
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“…The modeling follows the suggestion of a Bayesian approach (ROSSI et al, 2010;ROSSI, 2011), t is the incubation time in the rumen, in hours.…”
Section: Methodsmentioning
confidence: 99%
“…The modeling follows the suggestion of a Bayesian approach (ROSSI et al, 2010;ROSSI, 20 considered that the observations follow a Normal distribution, in other words, in which k * is the rate of solids passage in the rumen, which value was set at 2, 5 and 8% per hour.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The modeling follows the Bayesian approach (ROSSI et al, 2010;ROSSI, 2011), it was considered that the observations follow Normal distribution, that is, For each parameter, 310,000 values were generated in a MCMC (Markov Chain Monte Carlo) process, considering a sampling period discard of 10,000 initial values. Thus, the final sample obtained in leaps at every 30 values, contains 10,000 generated values.…”
Section: Methodsmentioning
confidence: 99%
“…As an alternative to the frequentist approach, the Bayesian methodology does not require the assumption of normality as a necessary condition and the inferences on the parameters are made on their posterior distribution. In this case, a model is supposed for each dataset and the parameters of each model are compared based on their posterior distributions (ROSSI et al, 2010).…”
Section: Introductionmentioning
confidence: 99%