We develop a Gaussian process (GP") framework for modeling mortality rates and mortality
improvement factors. GP regression is a nonparametric, data-driven approach for determining the
spatial dependence in mortality rates and jointly smoothing raw rates across dimensions, such as
calendar year and age. The GP model quanties uncertainty associated with smoothed historical
experience and generates full stochastic trajectories for out-of-sample forecasts. Our framework is well
suited for updating projections when newly available data arrives, and for dealing with edge" issues
where credibility is lower. We present a detailed analysis of Gaussian process model performance for
US mortality experience based on the CDC datasets. We investigate the interaction between mean
and residual modeling, Bayesian and non-Bayesian GP methodologies, accuracy of in-sample and out-
of-sample forecasting, and stability of model parameters. We also document the general decline, along
with strong age-dependency, in mortality improvement factors over the past few years, contrasting our
ndings with the Society of Actuaries (SOA") MP-2014 and -2015 models that do not fully re
ect
these recent trends.