Perfect sampling methods for random forests

Perfect Sampling of Random Spanning Trees

Let G be a weighted directed graph where the edge weights are non-negative real numbers. An arborescence or (directed) spanning tree with root r is a connected subgraph of G such that there is a unique directed path from v to r for each v ∈ V (G). The weight of a spanning tree T will be the product of the weights of the edges of T . Let Υ(G) be the probability distribution on the set of all spa...

A Perfect Sampling Method for Exponential Family Random Graph Models

Generation of deviates from random graph models with non-trivial edge dependence is an increasingly important problem. Here, we introduce a method which allows perfect sampling from random graph models in exponential family form (“exponential family random graph” models), using a variant of Coupling From The Past. We illustrate the use of the method via an application to the Markov graphs, a fa...

A Perfect Sampling Method for Exponential Random Graph Models

Sampling Methods for Random Subspace Domain Adaptation

Supervised classification tasks like Sentiment Analysis or text classification need labelled training data. These labels can be difficult to obtain, especially for complicated and ambiguous data like texts. Instead of labelling new data, domain adaptation tries to reuse already labelled data from related tasks as training data. We propose a greedy selection strategy to identify a small subset o...

A Perfect Sampling Algorithm of Random Walks with Forbidden Arcs

I will present a result obtained with Bruno Gaujal and Florence Perronin on simulation of grid Markov chain. Random walk are frequently used to model behavior of systems or complex randomized algorithm. one often needs to be able to compute or sample the stationary distribution. The problem we consider here is sampling the stationary distribution of a random walk on a multidimensional grid of g...