A Bivariate Normal Model of Mechanism Coupler-Point Position

1983 ◽  
Vol 105 (3) ◽  
pp. 599-605 ◽  
Author(s):  
G. R. Schade
Keyword(s):  
Monte Carlo ◽  
Probabilistic Models ◽  
Confidence Regions ◽  
Normal Model ◽  
Bivariate Normal ◽  
Point Position ◽  
Monte Carlo Techniques ◽  
Bivariate Normal Model ◽  
Four Bar Linkage ◽  
Mechanism Component

A bivariate normal model of the coupler-point position of a four-bar linkage is presented. Elliptical confidence regions are established as a function of the statistical properties of the mechanism component dimensions. A measure of mechanism merit based on the probability of deviation from mean path is developed. The probabilistic models are verified by Monte Carlo techniques.

Bivariate Normal Model: A Classroom Project

1983 ◽  
Vol 5 (1) ◽  
pp. 24-28
Author(s):  
M. W. MAXFIELD ◽  
B. C. LYON
Keyword(s):  
Normal Model ◽  
Bivariate Normal ◽  
Bivariate Normal Model ◽  
Classroom Project

Failure models indexed by two scales

1998 ◽  
Vol 30 (04) ◽  
pp. 1058-1072 ◽  
Author(s):  
Nozer D. Singpurwalla ◽  
Simon P. Wilson
Keyword(s):  
Monte Carlo ◽  
Survival Analysis ◽  
Probabilistic Models ◽  
Additive Hazards Model ◽  
Additive Hazards ◽  
Monte Carlo Techniques ◽  
Hazards Model ◽  
Single Scale ◽  
Failure Models ◽  
The Relationship

Much of the literature in reliability and survival analysis considers failure models indexed by a single scale. There are situations which require that failure be described by several scales. An example from reliability is items under warranty whose failure is recorded by time and amount of use. An example from survival analysis is the death of a mine worker which is noted by age and the duration of exposure to dust. This paper proposes an approach for developing probabilistic models indexed by two scales: time, and usage, a quantity that is related to time. The relationship between the scales is described by an additive hazards model. The evolution of usage is described by stochastic processes like the Poisson, the gamma and the Markov additive. The paper concludes with an application involving the setting of warranties. Two features differentiate this work from related efforts: a use of specific processes for describing usage, and a use of Monte Carlo techniques for generating the models.

A Logistic-Bivariate Normal Model for Overdispersed Two-State Markov Processes

Biometrics ◽  
1997 ◽  
Vol 53 (1) ◽  
pp. 358 ◽  
Author(s):  
Richard J. Cook ◽  
Edmund T. M. Ng
Keyword(s):  
Markov Processes ◽  
Normal Model ◽  
Bivariate Normal ◽  
Bivariate Normal Model

Mathematical Models for Describing the Clustering of Sociopathy and Hysteria in Families

1977 ◽  
Vol 130 (3) ◽  
pp. 294-297 ◽  
Author(s):  
T. P. Hutchinson ◽  
S. P. Satterthwaite
Keyword(s):  
Mathematical Models ◽  
Disease Transmission ◽  
Normal Model ◽  
Multifactorial Model ◽  
Bivariate Normal ◽  
Bivariate Normal Model ◽  
Familial Clustering

SummaryThe fitting of the multifactorial model of disease transmission to the familial clustering of hysteria and sociopathy by Cloninger et al (1) involved an approximation. This note reports the results of fitting the bivariate Normal model exactly and also two other distributions.

Failure models indexed by two scales

1998 ◽  
Vol 30 (4) ◽  
pp. 1058-1072 ◽  
Author(s):  
Nozer D. Singpurwalla ◽  
Simon P. Wilson
Keyword(s):  
Monte Carlo ◽  
Survival Analysis ◽  
Probabilistic Models ◽  
Additive Hazards Model ◽  
Additive Hazards ◽  
Monte Carlo Techniques ◽  
Hazards Model ◽  
Single Scale ◽  
Failure Models ◽  
The Relationship

Much of the literature in reliability and survival analysis considers failure models indexed by a single scale. There are situations which require that failure be described by several scales. An example from reliability is items under warranty whose failure is recorded by time and amount of use. An example from survival analysis is the death of a mine worker which is noted by age and the duration of exposure to dust.This paper proposes an approach for developing probabilistic models indexed by two scales: time, and usage, a quantity that is related to time. The relationship between the scales is described by an additive hazards model. The evolution of usage is described by stochastic processes like the Poisson, the gamma and the Markov additive. The paper concludes with an application involving the setting of warranties. Two features differentiate this work from related efforts: a use of specific processes for describing usage, and a use of Monte Carlo techniques for generating the models.

scholarly journals Objective priors for the bivariate normal model

2008 ◽  
Vol 36 (2) ◽  
pp. 963-982 ◽  
Author(s):  
James O. Berger ◽  
Dongchu Sun
Keyword(s):  
Normal Model ◽  
Bivariate Normal ◽  
Bivariate Normal Model
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