scholarly journals The General Projected Normal Distribution of Arbitrary Dimension: Modeling and Bayesian Inference

2017 ◽  
Vol 12 (1) ◽  
pp. 113-133 ◽  
Author(s):  
Daniel Hernandez-Stumpfhauser ◽  
F. Jay Breidt ◽  
Mark J. van der Woerd
Keyword(s):  
Bayesian Inference ◽  
Normal Distribution ◽  
Arbitrary Dimension ◽  
Projected Normal Distribution

A Bayesian regression model for circular data based on the projected normal distribution

2011 ◽  
Vol 11 (3) ◽  
pp. 185-201 ◽  
Author(s):  
Gabriel Nuñez-Antonio ◽  
Eduardo Gutiérrez-Peña ◽  
Gabriel Escarela
Keyword(s):  
Regression Model ◽  
Normal Distribution ◽  
Bayesian Regression ◽  
Circular Data ◽  
Projected Normal Distribution

scholarly journals Bayesian hidden Markov modelling using circular-linear general projected normal distribution

2015 ◽  
Vol 26 (2) ◽  
pp. 145-158 ◽  
Author(s):  
Gianluca Mastrantonio ◽  
Antonello Maruotti ◽  
Giovanna Jona-Lasinio
Keyword(s):  
Normal Distribution ◽  
Hidden Markov ◽  
Markov Modelling ◽  
Projected Normal Distribution

Bayesian inference for quantiles of the log‐normal distribution

2020 ◽  
Vol 62 (8) ◽  
pp. 1997-2012
Author(s):  
Aldo Gardini ◽  
Carlo Trivisano ◽  
Enrico Fabrizi
Keyword(s):  
Bayesian Inference ◽  
Normal Distribution ◽  
Log Normal ◽  
Log Normal Distribution

scholarly journals Modelling complex geological angular data with the Projected Normal distribution and mixtures of von Mises distributions

2013 ◽  
Vol 5 (2) ◽  
pp. 2181-2202
Author(s):  
R. M. Lark ◽  
D. Clifford ◽  
C. N. Waters
Keyword(s):  
Normal Distribution ◽  
Data Sets ◽  
Statistical Distributions ◽  
Data Set ◽  
Geological Interpretation ◽  
The Earth ◽  
Angular Data ◽  
Von Mises ◽  
Special Case ◽  
Projected Normal Distribution

Abstract. Angular data are commonly encountered in the earth sciences and statistical descriptions and inferences about such data are necessary in structural geology. In this paper we compare two statistical distributions appropriate for complex angular data sets: the mixture of von Mises and the projected normal distribution. We show how the number of components in a mixture of von Mises distribution may be chosen, and how one may chose between the projected normal distribution and mixture of von Mises for a particular data set. We illustrate these methods with some structural geological data, showing how the fitted models can complement geological interpretation and permit statistical inference. One of our data sets suggests a special case of the projected normal distribution which we discuss briefly.

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