I’m a statistician + data scientist in London with interests in statistics, machine learning, computer science, mathematics, science & scientific philosophy.
Proficient with R, Python, Stan & Tensorflow. Know a bit of C++ and general comp sci. Studied for an MPhil (Cambridge) and a BSc Hons (Edinburgh) in subjects that may or may not resemble statistics.
I’ll try to give examples of efficient gaussian process computation here, like the vec trick (Kronecker product trick), efficient toeliptz and circulant matrix computations, RTS smoothing and Kalman filtering using state space representations, and so on.
Very untidy first working draft of the idea mentioned on the efficient computation page. Here, I fit a spectral mixture to some audio data to build a “generative model” for audio. I’ll implement efficient sampling later, and I’ll replace the arbitrary way this is trained with an LSTM-RNN to go straight from text/spectrograms to waveforms.
First of my experiments on audio modelling using gaussian processes. Here, I construct a GP that, when sampled, plays middle c the way a grand piano would.
… using Stan & HMC
A Minimal Working Example