4 citations · 7 across the 6 of their papers we have counts for
6 papers
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
Fabian Zaiser, Andrzej S. Murawski, Luke Ong
We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to a large class of discrete inference problems, even with infinite su…
Nonparametric Involutive Markov Chain Monte Carlo
Carol Mak, Fabian Zaiser, Luke Ong
A challenging problem in probabilistic programming is to develop inference algorithms that work for arbitrary programs in a universal probabilistic programming language (PPL). We p…
Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming
Raven Beutner, Luke Ong, Fabian Zaiser
We propose a new method to approximate the posterior distribution of probabilistic programs by means of computing guaranteed bounds. The starting point of our work is an interval-b…
Nonparametric Hamiltonian Monte Carlo
Carol Mak, Fabian Zaiser, Luke Ong
Probabilistic programming uses programs to express generative models whose posterior probability is then computed by built-in inference engines. A challenging goal is to develop ge…
The Extended Theory of Trees and Algebraic (Co)datatypes
Fabian Zaiser, C. -H. Luke Ong
The first-order theory of finite and infinite trees has been studied since the eighties, especially by the logic programming community. Following Djelloul, Dao and Frühwirth, we co…
The Approximation Ratio of the 2-Opt Heuristic for the Metric Traveling Salesman Problem
Stefan Hougardy, Fabian Zaiser, Xianghui Zhong
The 2-Opt heuristic is one of the simplest algorithms for finding good solutions to the metric Traveling Salesman Problem. It is the key ingredient to the well-known Lin-Kernighan…