Results 101 to 110 of about 377,664 (139)

Bayesian inference on high-dimensional multivariate binary responses. [PDF]

J Am Stat Assoc
Chakraborty A, Ou R, Dunson DB.
europepmc   +1 more source

An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression. [PDF]

Sensors (Basel)
Rong Z, Li Y, Wu L, Zhang C, Li J.
europepmc   +1 more source

Classical and Bayesian inference for the new four-parameter Lomax distribution with applications. [PDF]

Heliyon
Abdullah M, Ahsan-Ul-Haq M, Alomair AM, Alomair MA.   +3 more
europepmc   +1 more source

Rapid Changes in Risk Preferences Originate from Bayesian Inference on Parietal Magnitude Representations

de Hollander G, Grueschow M, Hennel F, Ruff CC.   +3 more
europepmc   +1 more source

The parieto-occipital cortex is a candidate neural substrate for the human ability to approximate Bayesian inference. [PDF]

Commun Biol
Singletary NM, Gottlieb J, Horga G.
europepmc   +1 more source

Robustification of Bayesian-Inference-Based Gait Estimation for Lower-limb Wearable Robots. [PDF]

IEEE Robot Autom Lett
Hsu TW, Gregg RD, Thomas GC.
europepmc   +1 more source

Bayesian inference of sex-specific mortality profiles and product yields from unsexed cattle zooarchaeological remains

Diekmann Y, Gillis R, Lu Z, Rudzinski A, Thomas MG.   +4 more
europepmc   +1 more source

Correction: Bayesian inference of kinetic schemes for ion channels by Kalman filtering.

Elife
Münch JL, Paul F, Schmauder R, Benndorf K.   +3 more
europepmc   +1 more source

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Inference in Bayesian networks

Nature Biotechnology, 2006
Bayesian networks are increasingly important for integrating biological data and for inferring cellular networks and pathways. What are Bayesian networks and how are they used for inference?
Andrew J. Bulpitt, James R. Bradford, David R. Westhead, Chris J. Needham   +3 more
openaire   +3 more sources

Bayesian Inference

2012
This chapter provides an overview of the Bayesian approach to data analysis, modeling, and statistical decision making. The topics covered go from basic concepts and definitions (random variables, Bayes' rule, prior distributions) to various models of general use in biology (hierarchical models, in particular) and ways to calibrate and use them (MCMC ...
openaire   +4 more sources