Gaussian Processes with Spectral Mixture Kernels to Implicitly Capture Hidden Structure from Data

This study proposed a Bayesian nonparametric framework to capture implicitly hidden structure in time-series having limited data. The proposed framework, a Gaussian process with a spectral mixture kernel, was applied to time-series process for insider-threat detection. The proposed framework addresses two current challenges when analyzing quite noisy time-series having limited data whereby the time series are visualized for noticeable structure such as periodicity, growing or decreasing trends and hard coding them into pre-specified functional forms.

ICYMI: Probabilistic Programming Roundup November 2017

In case you missed them, here are some articles from November 2017 of particular interest to users of Probabilistic Programming Languages(PPL).

ICYMI: My talk on Introduction to Probabilistic Machine Learning

Incase you missed it, here is a recording of my talk on Introduction to Probabilisitic Machine Learning at the Las Vegas R & Data Science Meetup groups. I introduced probabilistic machine learning and probabilistic programming with Stan. I discussed the basics of machine learning from a probabilistic/Bayesian perspective and contrasted it with traditional/algorithmic machine learning

In case you missed it: My Webinar on Model-Based Machine Learning

In case you missed my free webinar on “Model-Based Machine Learning”, ┬áhere is the recording. If you have any questions, please do not hesitate to contact me.