Controlling procedural modeling programs with stochastically-ordered sequential Monte Carlo

2015

74 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

Citations in the corpus

• Foundation Posteriors for Approximate Probabilistic Inference (2022, neutral): “Some require users to assist in the inference process by programmatically specifying conditional independencies [7] or hand-crafting variational distributions [52, 7, 17].”
• An Introduction to Probabilistic Programming (2018, background): “The constrained procedural graphics (Ritchie et al., 2015) is a visually compelling and elucidating application of probabilistic programming.”
• C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching (2015, methodology): “We use a simple grammar-like program for tree skeletons presented in prior work, conditioning its output to be volumetrically similar to a target shape [16].”
• Inference Networks for Sequential Monte Carlo in Graphical Models (2016, background): “, 2014), procedural generative graphics (Ritchie et al., 2015), and general probabilistic programs (Wood et al.”
• Meta-Learning an Inference Algorithm for Probabilistic Programs (2021, background): “One key objective of probabilistic programming is to automate reasoning about probabilistic models from diverse domains (Ritchie et al., 2015; Perov & Wood, 2016; Baydin et al., 2019; Schaechtle et al., 2016; Cusumano-Towner et al., 2017; Saad & Mansinghka, 2016; Kulkarni et al., 2015; Young et…”