Statistical modeling of directional data using a robust hierarchical von mises distribution model: perspectives for wind energy

2022 ◽  
Author(s):  
Said Benlakhdar ◽  
Mohammed Rziza ◽  
Rachid Oulad Haj Thami
Keyword(s):  
Wind Energy ◽  
Statistical Modeling ◽  
Directional Data ◽  
Distribution Model ◽  
Von Mises Distribution ◽  
Von Mises

An extension of the von mises distribution

1982 ◽  
Vol 11 (15) ◽  
pp. 1695-1706 ◽  
Author(s):  
E.A. Yfantis ◽  
L.E. Borgman
Keyword(s):  
Von Mises Distribution ◽  
Von Mises

Time-efficient Bayesian Inference for a (Skewed) Von Mises Distribution on the Torus in a Deep Probabilistic Programming Language

2021 ◽  
Author(s):  
Ola Ronning ◽  
Christophe Ley ◽  
Kanti V. Mardia ◽  
Thomas Hamelryck
Keyword(s):  
Bayesian Inference ◽  
Programming Language ◽  
Von Mises Distribution ◽  
Probabilistic Programming ◽  
Von Mises

Error-in-variables model of Malacca wind direction data with the von Mises distribution in southwest monsoon

2021 ◽  
Vol 15 (9) ◽  
pp. 471-479
Author(s):  
Nurkhairany Amyra Mokhtar ◽  
Basri Badyalina ◽  
Kerk Lee Chang ◽  
Fatin Farazh Ya'acob ◽  
Ahmad Faiz Ghazali ◽  
...  
Keyword(s):  
Wind Direction ◽  
Southwest Monsoon ◽  
Von Mises Distribution ◽  
Error In Variables ◽  
Von Mises

scholarly journals STATISTICAL MODEL FOR THE RELATIONSHIP BETWEEN MAIZE KERNEL ORIENTATION AND SEED LEAF AZIMUTH

2015 ◽  
Vol 52 (3) ◽  
pp. 359-370
Author(s):  
ADRIAN KOLLER ◽  
GUILHERME TORRES ◽  
MICHAEL BUSER ◽  
RANDY TAYLOR ◽  
BILL RAUN ◽  
...  
Keyword(s):  
Simulation Models ◽  
Circular Statistics ◽  
Von Mises Distribution ◽  
Canopy Closure ◽  
Canopy Architecture ◽  
Maize Kernel ◽  
Angular Data ◽  
Significant Yield ◽  
Von Mises ◽  
The Relationship

SUMMARYHand-planted plots of across-row-oriented corn seeds (Zeamays L.) produce highly structured leaf canopies and have shown significant yield advantage over randomly planted plots in prior studies. For further investigation of the phenomenon by simulation, the objective of this study was to develop a probabilistic model for the correlation between seed orientation and initial plant orientation. In greenhouse trials, the azimuthal orientation of kernels of four different hybrids was recorded at planting. At collar setting of the seed leaf, the orientation of the seed leaf was determined and the angular data subjected to the analytical methods of circular statistics. The results indicate that the correlation between seed azimuth and seed leaf azimuth can be described by a von Mises distribution. The probabilistic seed to seed leaf azimuth model described herein may be implemented in simulation models to investigate the effect of canopy architecture, canopy closure and light interception efficiency of corn under conditions of seed oriented planting.

Multisample Tests for the von Mises Distribution

1972 ◽  
Vol 67 (338) ◽  
pp. 456-461 ◽  
Author(s):  
M. A. Stephens
Keyword(s):  
Von Mises Distribution ◽  
Von Mises

scholarly journals Computational Representation of White Matter Fiber Orientations

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Adelino R. Ferreira da Silva
Keyword(s):  
White Matter ◽  
Angular Resolution ◽  
Diffusion Imaging ◽  
Real Data ◽  
Orientation Distribution ◽  
Directional Data ◽  
High Angular Resolution ◽  
Clustering Data ◽  
Von Mises ◽  
Computational Representation

We present a new methodology based on directional data clustering to represent white matter fiber orientations in magnetic resonance analyses for high angular resolution diffusion imaging. A probabilistic methodology is proposed for estimating intravoxel principal fiber directions, based on clustering directional data arising from orientation distribution function (ODF) profiles. ODF reconstructions are used to estimate intravoxel fiber directions using mixtures of von Mises-Fisher distributions. The method focuses on clustering data on the unit sphere, where complexity arises from representing ODF profiles as directional data. The proposed method is validated on synthetic simulations, as well as on a real data experiment. Based on experiments, we show that by clustering profile data using mixtures of von Mises-Fisher distributions it is possible to estimate multiple fiber configurations in a more robust manner than currently used approaches, without recourse to regularization or sharpening procedures. The method holds promise to support robust tractographic methodologies and to build realistic models of white matter tracts in the human brain.

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