Search Results for author:
Found 6 papers, 4 papers with code
Scalable Spatiotemporal Prediction with Bayesian Neural Fields
Spatiotemporal datasets, which consist of spatially-referenced time series, are ubiquitous in many scientific and business-intelligence applications, such as air pollution monitoring, disease tracking, and cloud-demand forecasting.
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
We found that human evaluators often prefer the results from probabilistic search to results from a standard baseline.
A Probabilistic Programming Approach To Probabilistic Data Analysis
Probabilistic techniques are central to data analysis, but different approaches can be challenging to apply, combine, and compare.
Time Series Structure Discovery via Probabilistic Program Synthesis
There is a widespread need for techniques that can discover structure from time series data.
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes
Datasets with hundreds of variables and many missing values are commonplace.
Probabilistic Data Analysis with Probabilistic Programming
This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques.
