Multivariate statistical simulation
Multivariate statistical simulation
A rejection technique for sampling from T-concave distributions
ACM Transactions on Mathematical Software (TOMS)
Random variate generation for multivariate unimodal densities
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A rejection technique for sampling from log-concave multivariate distributions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A sweep-plane algorithm for generating random tuples in simple polytopes
Mathematics of Computation
Proceedings of the 32nd conference on Winter simulation
Fast simulation of truncated Gaussian distributions
Statistics and Computing
Hi-index | 0.00 |
Different automatic (also called universal or black-box) methods have been suggested to sample from univariate log-concave distributions. Our new automatic algorithm for bivariate log-concave distributions is based on the method of transformed density rejection. In order to construct a hat function for a rejection algorithm the bivariate density is transformed by the logarithm into a concave function. Then it is possible to construct a dominating function by taking the minimum of several tangent planes, which are by exponentiation transformed back into the original scale. The choice of the points of contact is automated using adaptive rejection sampling. This means that points that are rejected by the rejection algorithm can be used as additional points of contact. The article describes the details how this main idea can be used to construct Algorithm ALC2D that can generate random pairs from all bivariate log-concave distributions with known domain, computable density, and computable partial derivatives.