Exact and Approximate Sampling by Systematic Stochastic Search

By Vikash K. Mansinghka, Daniel M. Roy, Eric Jonas, Josh Tenenbaum. 2009

18 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods, Algorithms & Complexity

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Probabilistic & Bayesian Methods

1. Chris J. Maddison
2. Daniel Tarlow
3. Guillaume Bouchard
4. Simon Carter
5. Marc Dymetman
6. Tom Minka
7. Zoubin Ghahramani
8. Masataro Asai

In Algorithms & Complexity

1. Daniel Tarlow
2. Chris J. Maddison
3. Tom Minka

Citations in the corpus

• A* Sampling (2014, methodology): “Some are specialized to specific distributions [5], but exact generic methods are based either on (adaptive) rejection sampling [6, 7, 8] or Markov Chain Monte Carlo (MCMC) methods where convergence to the stationary distribution can be guaranteed [9, 10, 11].”
• The OS* Algorithm: a Joint Approach to Exact Optimization and Sampling (2012, methodology): “Also in the context of rejection sampling, [Mansinghka et al., 2009] considers the case of a probabilistic graphical model; it introduces an heuristically determined order of the variables in this model and uses this (fixed) order to define a sequence of exact samplers over an increasing set of…”