Bayesian statistics

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Bayesian statistics

[¦bāz·ē·ən stə′tis·tiks]
(statistics)
An approach to statistics in which estimates are based on a synthesis of a prior distribution and current sample data.
References in periodicals archive ?
The team said the process is based on a set of tools relying on several relatively simple lab tests combined with computer modeling of the physical properties of the proposed materials, as well as additional modeling based on a statistical method called Bayesian inference, which allows the estimates of each parameter to be updated based on each new measurement.
This article utilized two advanced prediction methods to predict the probability of a flight-delay incident--data mining using the decision tree and data mining using Bayesian inference.
Bayesian inference is the process or the logic to calculate or revise the probability of belief (hypostasis).
On probability, using the principles of Bayesian inference, he shows the effect of false positives and why having prior knowledge before a diagnosis is important.
The best model suggested by the Akaike information criterion (AIC) was used to reconstruct phylogenetic tree in Bayesian inference (BI) and Maximum Likelihood (ML) analysis.
Bayesian inference is described in the literature as a suitable method for fault detection and fault classification in condition monitoring systems [7], [8].
The approach will use Bayesian Inference to incorporate a priori knowledge of the design alternatives that support the elicited belief of the decision maker before obtaining any information on the design alternatives.
He covers the modeling of survival data, the progressive three-state survival model, general multi-state survival model, frailty models, Bayesian inference for multi-state survival models, residual state-specific life expectancy, and a wide variety of other related subjects.
Analysis of Cell Lineage Trees by Exact Bayesian Inference Identifies Negative Autoregulation of Nanog in Mouse Embryonic Stem Cells," Cell Systems, 2016; 3 (5): 480 DOI: 10.
What's more, two saliency maps can be merged together well depending on the Bayesian inference.
The Bayesian inference dilemma frequently faced by patients and their physicians was recently highlighted in Science in a perspective article by Operskalski and Barbey (2), Through the psychological sciences of clinical reasoning errors, these authors offer additional insights into how we think about probabilities and risk.

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