15: Scalable Learning for Partially Censored Gaussian Processes

Music City Center 

Gaussian processes (GPs), known for their flexibility, uncertainty quantification, and interpretability, are particularly useful for modeling environmental variables across space and time. However, datasets with censoring, arising from detection limits of sensors, pose computational challenges. Common approaches, such as substituting censored values with detection limits or Markov chain Monte Carlo (MCMC) methods, can introduce biases or inefficiencies. Our work develops linear-complexity solutions for multivariate normal (MVN) probability estimation and sampling from truncated MVN (TMVN) distributions. Accompanying R packages, VeccTMVN and nntmvn are developed and published. Future academic travels would help me promote the scalable inference for partially censored datasets and the develop computation softwares.