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In
probability theory Probability theory is the branch of mathematics concerned with probability. Although there are several different probability interpretations, probability theory treats the concept in a rigorous mathematical manner by expressing it through a set o ...
and
statistics Statistics (from German language, German: ''wikt:Statistik#German, Statistik'', "description of a State (polity), state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of ...
, the bivariate von Mises distribution is a
probability distribution In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon i ...
describing values on a
torus In geometry, a torus (plural tori, colloquially donut or doughnut) is a surface of revolution generated by revolving a circle in three-dimensional space about an axis that is coplanar with the circle. If the axis of revolution does not tou ...
. It may be thought of as an analogue on the torus of the
bivariate normal distribution In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions. One ...
. The distribution belongs to the field of
directional statistics Directional statistics (also circular statistics or spherical statistics) is the subdiscipline of statistics that deals with directions (unit vectors in Euclidean space, R''n''), axes (lines through the origin in R''n'') or rotations in R''n''. M ...
. The general bivariate von Mises distribution was first proposed by
Kanti Mardia Kantilal Vardichand "Kanti" Mardia (born 1935) is an Indian-British statistician specialising in directional statistics, multivariate analysis, geostatistics, statistical bioinformatics and statistical shape analysis. He was born in Sirohi, Ra ...
in 1975. One of its variants is today used in the field of
bioinformatics Bioinformatics () is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combi ...
to formulate a probabilistic model of
protein structure Protein structure is the three-dimensional arrangement of atoms in an amino acid-chain molecule. Proteins are polymers specifically polypeptides formed from sequences of amino acids, the monomers of the polymer. A single amino acid monomer ma ...
in atomic detail, such as backbone-dependent rotamer libraries.


Definition

The bivariate von Mises distribution is a
probability distribution In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon i ...
defined on the
torus In geometry, a torus (plural tori, colloquially donut or doughnut) is a surface of revolution generated by revolving a circle in three-dimensional space about an axis that is coplanar with the circle. If the axis of revolution does not tou ...
, S^1 \times S^1 in \mathbb^3. The probability density function of the general bivariate von Mises distribution for the angles \phi, \psi \in , 2\pi/math> is given by : f(\phi, \psi) \propto \exp \kappa_1 \cos(\phi - \mu) + \kappa_2 \cos(\psi - \nu) + (\cos(\phi-\mu), \sin(\phi-\mu)) \mathbf (\cos(\psi - \nu), \sin(\psi - \nu))^T where \mu and \nu are the means for \phi and \psi, \kappa_1 and \kappa_2 their concentration and the matrix \mathbf \in \mathbb(2,2) is related to their correlation. Two commonly used variants of the bivariate von Mises distribution are the sine and cosine variant. The cosine variant of the bivariate von Mises distribution has the probability density function : f(\phi, \psi) = Z_c(\kappa_1, \kappa_2, \kappa_3) \ \exp \kappa_1 \cos(\phi - \mu) + \kappa_2 \cos(\psi - \nu) - \kappa_3 \cos(\phi - \mu - \psi + \nu) where \mu and \nu are the means for \phi and \psi, \kappa_1 and \kappa_2 their concentration and \kappa_3 is related to their correlation. Z_c is the normalization constant. This distribution with \kappa_3=0 has been used for kernel density estimates of the distribution of the protein dihedral angles \phi and \psi. The sine variant has the probability density function : f(\phi, \psi) = Z_s(\kappa_1, \kappa_2, \kappa_3) \ \exp \kappa_1 \cos(\phi - \mu) + \kappa_2 \cos(\psi - \nu) + \kappa_3 \sin(\phi - \mu) \sin(\psi - \nu) where the parameters have the same interpretation.


See also

*
Von Mises distribution In probability theory and directional statistics, the von Mises distribution (also known as the circular normal distribution or Tikhonov distribution) is a continuous probability distribution on the circle. It is a close approximation to the wr ...
, a similar distribution on the one-dimensional unit circle *
Kent distribution In directional statistics, the Kent distribution, also known as the 5-parameter Fisher–Bingham distribution (named after John T. Kent, Ronald Fisher, and Christopher Bingham), is a probability distribution on the unit sphere (2-sphere ''S''2 in ...
, a related distribution on the two-dimensional unit sphere *
von Mises–Fisher distribution In directional statistics, the von Mises–Fisher distribution (named after Richard von Mises and Ronald Fisher), is a probability distribution on the (p-1)-sphere in \mathbb^. If p=2 the distribution reduces to the von Mises distribution on the ci ...
*
Directional statistics Directional statistics (also circular statistics or spherical statistics) is the subdiscipline of statistics that deals with directions (unit vectors in Euclidean space, R''n''), axes (lines through the origin in R''n'') or rotations in R''n''. M ...


References

{{ProbDistributions, directional Continuous distributions Directional statistics Multivariate continuous distributions