Bayesian approach

Bayesian approach

An approach to data analysis which provides a posterior probability distribution for some parameter (e.g., treatment effect) derived from the observed data and a prior probability distribution for the parameter. The posterior distribution forms the basis for statistical inference.
References in periodicals archive ?
The Bayesian approach provides insights that are not feasible with traditional meta-analysis and reveals the likelihood of an outcome, making it easier for a doctor or patient to understand the results more clearly.
The bandwidth is predicted using a Bayesian-Gaussian approach in contrast to the Bayesian approach used by Lai et al [7].
Finally, the magnitude-based inference is proposed as a possible solution, which melts the pragmatism of the Bayesian approach with the rigor of the Neyman-Pearson approach.
Model refinement and Bayesian approach to estimating interstrain variability in concentration-time profiles of TCE metabolites in mouse serum.
The topics include quantitative genetics and general issues in QTL mapping, multiple interval mapping, mapping with dense markers, and a Bayesian approach.
To deal with this issue, Titelbaum presents the Certainty-Loss Framework, a modified Bayesian approach that provides a way to model and accurately represent rational requirements on agents who undergo certainty loss.
The Bayesian approach starts with a simple method--drawn from probability theory--for describing the varying confidence levels a fact finder might have regarding the probative force of a piece of evidence.
The Bayesian approach to probability and statistics is not the only one, and it is not always intuitive.
The semi-parametric Bayesian approach is effective to overcome the problem of missing repeated observations.
On unblinding the dosing information, we observed that the adaptive Bayesian approach was able to identify 8 of the 20 patients during the drug-administration period for a drug without prior documentation of renal impairment.
More recently, a Bayesian approach using Gibbs sampling was proposed to overcome the shortage of degrees of freedom by treating the epistatic effects as random effects (Lee and Park, 2007).
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