Adaptive Rejection Sampling Methods

2018 ◽  
pp. 115-157
Author(s):  
Luca Martino ◽  
David Luengo ◽  
Joaquín Míguez
Keyword(s):  
Sampling Methods ◽  
Rejection Sampling ◽  
Adaptive Rejection Sampling

scholarly journals Parsimonious adaptive rejection sampling

2017 ◽  
Vol 53 (16) ◽  
pp. 1115-1117 ◽  
Author(s):  
L. Martino
Keyword(s):  
Rejection Sampling ◽  
Adaptive Rejection Sampling

scholarly journals Perfect sampling methods for random forests

2008 ◽  
Vol 40 (03) ◽  
pp. 897-917 ◽  
Author(s):  
Hongsheng Dai
Keyword(s):  
Random Forests ◽  
Probability Space ◽  
Sampling Methods ◽  
Weighted Graph ◽  
Rejection Sampling ◽  
Perfect Sampling ◽  
Target Distribution ◽  
The Past ◽  
Coupling From The Past ◽  
Vertex Set

A weighted graph G is a pair (V, ℰ) containing vertex set V and edge set ℰ, where each edge e ∈ ℰ is associated with a weight We . A subgraph of G is a forest if it has no cycles. All forests on the graph G form a probability space, where the probability of each forest is proportional to the product of the weights of its edges. This paper aims to simulate forests exactly from the target distribution. Methods based on coupling from the past (CFTP) and rejection sampling are presented. Comparisons of these methods are given theoretically and via simulation.

scholarly journals Exact Simulation for Diffusion Bridges: An Adaptive Approach

2014 ◽  
Vol 51 (02) ◽  
pp. 346-358
Author(s):  
Hongsheng Dai
Keyword(s):  
Brownian Motion ◽  
Sampling Methods ◽  
Simulation Method ◽  
New Method ◽  
Sampling Techniques ◽  
Rejection Sampling ◽  
Exact Simulation ◽  
Adaptive Approach

Exact simulation approaches for a class of diffusion bridges have recently been proposed based on rejection sampling techniques. The existing rejection sampling methods may not be practical owing to small acceptance probabilities. In this paper we propose an adaptive approach that improves the existing methods significantly under certain scenarios. The idea of the new method is based on a layered process, which can be simulated from a layered Brownian motion with reweighted layer probabilities. We will show that the new exact simulation method is more efficient than existing methods theoretically and via simulation.

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