Recursive Bayesian Algorithm with Covariance Resetting for Identification of Box–Jenkins Systems with Non-uniformly Sampled Input Data

2015 ◽  
Vol 35 (3) ◽  
pp. 919-932 ◽  
Author(s):  
Shaoxue Jing ◽  
Tianhong Pan ◽  
Zhengming Li
Keyword(s):  
Input Data ◽  
Bayesian Algorithm ◽  
Covariance Resetting ◽  
Recursive Bayesian

scholarly journals Use of Recursive Bayesian Algorithm to Optimise Coronary Artery Disease Diagnostic Pathways

2011 ◽  
Vol 20 ◽  
pp. S45
Author(s):  
J. Rivers ◽  
J. Lefevre ◽  
M. Waterhouse ◽  
I. Smith ◽  
K. Mengersen
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Bayesian Algorithm ◽  
Artery Disease ◽  
Diagnostic Pathways ◽  
Recursive Bayesian

Transmission Electron Diffraction Analysis by Computer

1970 ◽  
Vol 28 ◽  
pp. 40-41
Author(s):  
R.A. Ploc ◽  
G.H. Keech
Keyword(s):  
Electron Diffraction ◽  
Input Data ◽  
Reciprocal Lattice ◽  
Computer Programme ◽  
Transmission Electron Diffraction ◽  
Usual Procedure ◽  
Transmission Electron ◽  
Complex Shapes ◽  
Computer Input ◽  
Diffraction Effects

An unambiguous analysis of transmission electron diffraction effects requires two samplings of the reciprocal lattice (RL). However, extracting definitive information from the patterns is difficult even for a general orthorhombic case. The usual procedure has been to deduce the approximate variables controlling the formation of the patterns from qualitative observations. Our present purpose is to illustrate two applications of a computer programme written for the analysis of transmission, selected area diffraction (SAD) patterns; the studies of RL spot shapes and epitaxy.When a specimen contains fine structure the RL spots become complex shapes with extensions in one or more directions. If the number and directions of these extensions can be estimated from an SAD pattern the exact spot shape can be determined by a series of refinements of the computer input data.

Bankruptcy Model Construction and its Limitation in Input Data Quality

2019 ◽  
Vol 10 (2) ◽  
pp. 117-125
Author(s):  
Dana Kubíčková ◽  
◽  
Vladimír Nulíček ◽  
Keyword(s):  
Data Quality ◽  
Normal Distribution ◽  
Input Data ◽  
Model Construction ◽  
The Third ◽  
Third Stage ◽  
Discriminant Analyses ◽  
One Year ◽  
The University ◽  
Multivariate Discriminant

The aim of the research project solved at the University of Finance and administration is to construct a new bankruptcy model. The intention is to use data of the firms that have to cease their activities due to bankruptcy. The most common method for bankruptcy model construction is multivariate discriminant analyses (MDA). It allows to derive the indicators most sensitive to the future companies’ failure as a parts of the bankruptcy model. One of the assumptions for using the MDA method and reassuring the reliable results is the normal distribution and independence of the input data. The results of verification of this assumption as the third stage of the project are presented in this article. We have revealed that this assumption is met only in a few selected indicators. Better results were achieved in the indicators in the set of prosperous companies and one year prior the failure. The selected indicators intended for the bankruptcy model construction thus cannot be considered as suitable for using the MDA method.

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