The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints.
|Author||Gert van Valkenhoef, Tommi Tervonen|
|Date of publication||2016-12-23 11:57:17|
|Maintainer||Gert van Valkenhoef <email@example.com>|
bbReject: Bounding box rejection sampler
createBoundBox: Calculate a bounding box
createSeedPoint: Generate a seed point
createTransform: Create transformation matrices
eliminateRedundant: Eliminate redundant linear constraints
findExtremePoints: Find extreme points
findFace: Find the closest face (constraint) to an interior point of a...
findInteriorPoint: Find an interior point
findVertices: Find vertices of the polytope
har: "Hit and Run" sampler
har-constraint: Constraint formulation utility functions
hitandrun: "Hit and Run" sampler
hitandrun-package: "Hit and Run" sampling
hypersphere.sample: Sample uniformly from an n-hypersphere
sab: "Shake and Bake" sampler
shakeandbake: "Shake and Bake" sampler
simplex.createConstraints: Create constraints that define the (n-1)-simplex
simplex.createTransform: Transform points on an (n-1)-simplex to n-dimensional space
simplex.sample: Sample uniformly from a simplex
solution.basis: Calculate the basis for the solution space of a system of...
transformConstraints: Apply a transformation to a set of linear constraints.