Search Results for author:
Found 4 papers, 1 papers with code
Correcting for heterogeneity in real-time epidemiological indicators
We describe the problem of spatial and temporal heterogeneity in these signals derived from these data sources, where spatial and/or temporal biases are present.
Nonnegative spatial factorization
Gaussian processes are widely used for the analysis of spatial data due to their nonparametric flexibility and ability to quantify uncertainty, and recently developed scalable approximations have facilitated application to massive datasets.
Review of Probability Distributions for Modeling Count Data
Count data take on non-negative integer values and are challenging to properly analyze using standard linear-Gaussian methods such as linear regression and principal components analysis.
Generalized Principal Component Analysis
Generalized principal component analysis (GLM-PCA) facilitates dimension reduction of non-normally distributed data.
