WordNet

of or relating to or in terms of a parameter; "parametric equation"

English Journal

Bayesian non-parametrics and the probabilistic approach to modelling.

Ghahramani Z.Author information Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK. zoubin@eng.cam.ac.ukAbstractModelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic models. Flexibility is achieved through the use of Bayesian non-parametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian non-parametrics. The survey covers the use of Bayesian non-parametrics for modelling unknown functions, density estimation, clustering, time-series modelling, and representing sparsity, hierarchies, and covariance structure. More specifically, it gives brief non-technical overviews of Gaussian processes, Dirichlet processes, infinite hidden Markov models, Indian buffet processes, Kingman's coalescent, Dirichlet diffusion trees and Wishart processes.
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences.Philos Trans A Math Phys Eng Sci.2012 Dec 31;371(1984):20110553. doi: 10.1098/rsta.2011.0553. Print 2013 Feb 13.
Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic ap
PMID 23277609

Changes in CT cerebral blood flow and volume associated with rapid maxillary expansion in a rabbit model.

Li Q, Wang W, Zhang Q, Wang L.Author information Institute of Stomatology, Nanjing Medical University, Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing, PR China.AbstractOBJECTIVE: To evaluate hemodynamic changes of the brain under the high forces of a rapid maxillary expansion (RME) appliance in a rabbit model.
The Angle orthodontist.Angle Orthod.2012 May;82(3):418-23. doi: 10.2319/051211-328.1. Epub 2011 Sep 7.
OBJECTIVE: To evaluate hemodynamic changes of the brain under the high forces of a rapid maxillary expansion (RME) appliance in a rabbit model.MATERIALS AND METHODS: Twenty-four male New Zealand white rabbits were selected (12 weeks old, mean weight 3.01 ± 0.22 kg). A modified acrylic resin bonded
PMID 21899376

Rubbery Polya Tree.

Nieto-Barajas LE1, Müller P2.Author information 1Department of Statistics, ITAM.2Department of Mathematics, The University of Texas at Austin.AbstractPolya trees (PT) are random probability measures which can assign probability 1 to the set of continuous distributions for certain specifications of the hyperparameters. This feature distinguishes the PT from the popular Dirichlet process (DP) model which assigns probability 1 to the set of discrete distributions. However, the PT is not nearly as widely used as the DP prior. Probably the main reason is an awkward dependence of posterior inference on the choice of the partitioning subsets in the definition of the PT. We propose a generalization of the PT prior that mitigates this undesirable dependence on the partition structure, by allowing the branching probabilities to be dependent within the same level. The proposed new process is not a PT anymore. However, it is still a tail-free process and many of the prior properties remain the same as those for the PT.
Scandinavian journal of statistics, theory and applications.Scand Stat Theory Appl.2012 Mar;39(1). doi: 10.1111/j.1467-9469.2011.00761.x.
Polya trees (PT) are random probability measures which can assign probability 1 to the set of continuous distributions for certain specifications of the hyperparameters. This feature distinguishes the PT from the popular Dirichlet process (DP) model which assigns probability 1 to the set of discrete
PMID 24368872