  We present xokde++, a state-of-the-art online kernel density estimation
approach that maintains Gaussian mixture models input data streams. The
approach follows state-of-the-art work on online density estimation, but was
redesigned with computational efficiency, numerical robustness, and
extensibility in mind. Our approach produces comparable or better results than
the current state-of-the-art, while achieving significant computational
performance gains and improved numerical stability. The use of diagonal
covariance Gaussian kernels, which further improve performance and stability,
at a small loss of modelling quality, is also explored. Our approach is up to
40 times faster, while requiring 90\% less memory than the closest
state-of-the-art counterpart.
