Search results
Results from the WOW.Com Content Network
The sample code in demo2DDataAssociation demonstrates how the algorithms can be used in a simple scenario. Python: The PDAF, JPDAF and other data association methods are implemented in Stone-Soup. [10] A tutorial demonstrates how the algorithms can be used. [11] [12]
For example, imagine that a model consists of three variables A, B, and C. A simple Gibbs sampler would sample from p(A | B,C), then p(B | A,C), then p(C | A,B). A collapsed Gibbs sampler might replace the sampling step for A with a sample taken from the marginal distribution p(A | C), with variable B integrated out in this case.
This infinite process is not dependent upon the starting shape being a triangle—it is just clearer that way. The first few steps starting, for example, from a square also tend towards a Sierpiński triangle. Michael Barnsley used an image of a fish to illustrate this in his paper "V-variable fractals and superfractals." [2] [3] Iterating from ...
If the software program does not generate the confidence band, it is approximately /, with N denoting the sample size. The autocorrelation function of a MA process becomes zero at lag q + 1 and greater, so we examine the sample autocorrelation function to see where it essentially becomes zero. We do this by placing the 95% confidence interval ...
It can be shown that if a system is described by a probability density in phase space, then Liouville's theorem implies that the joint information (negative of the joint entropy) of the distribution remains constant in time. The joint information is equal to the mutual information plus the sum of all the marginal information (negative of the ...
If the points in the joint probability distribution of X and Y that receive positive probability tend to fall along a line of positive (or negative) slope, ρ XY is near +1 (or −1). If ρ XY equals +1 or −1, it can be shown that the points in the joint probability distribution that receive positive probability fall exactly along a straight ...
Given a known joint distribution of two discrete random variables, say, X and Y, the marginal distribution of either variable – X for example – is the probability distribution of X when the values of Y are not taken into consideration. This can be calculated by summing the joint probability distribution over all values of Y.
Python: the KernelReg class for mixed data types in the statsmodels.nonparametric sub-package (includes other kernel density related classes), the package kernel_regression as an extension of scikit-learn (inefficient memory-wise, useful only for small datasets) R: the function npreg of the np package can perform kernel regression. [7] [8]