Examples of using Bayesian inference in English and their translations into Portuguese
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While"Bayesian inference" is sometimes held to include the approach to inference leading to optimal decisions,
Therefore, we performed bayesian inference and maximum parsimony analyses for seven different chloroplast dna regions,
Parsimony and bayesian inference analysis were conducted with 182 morphological
The inverse problem was solved by bayesian inference, using the markov chain monte carlo method(mcmc),
In recent years this has revolutionized the practicability of Bayesian inference methods, allowing a wide range of posterior distributions to be simulated
The dispute has become more complex since Bayesian inference has achieved respectability.
Components of(co)variance were estimated using an animal model by bayesian inference method.
11 microsatellite loci, and bayesian inference and coalescent model were used, combined with traditional statistics and methodology. the results support the.
in the last decade, we used bayesian inference techniques, along with monte carlo simulatio.
In addition, a phylogenetic tree was built by bayesian inference based on sequences of 16s rdna and coimtdna of some representatives of the genus.
using bayesian inference.
Estimates of the parameters and hyper-parameters of interest were obtained from the Monte Carlo Method via the Markov Prison MCMC where the WinBugs Win Bayesian Inference Using Gibbs Sampling version 1.4 statistical program has been implemented.
through the free software WinBUGS 1.4.0Win Bayesian inference Using Gibbs Sampling,
In contrast, Bayesian inference works in terms of conditional probabilities(i.e. probabilities conditional on the observed data),
In this case, the smart bet is"heads" because Bayesian inference from the empirical evidence- 21 heads in a row- suggests that the coin is likely to be biased toward heads.
Formally, Bayesian inference is calibrated with reference to an explicitly stated utility,
levenberg-marquardt and bayesian inference to estimate parameters of the advection-diffusion equation in the simulation of the instantaneous release of a non-conservative source.
In Bayesian inference, randomization is also of importance:
a threshold model for prol by bayesian inference.
as well as, to estimate the genetic parameters for reproductive traits using the bayesian inference methodology.