A conceptual guide
Gaussian processes (GPs) are a flexible class of nonparametric machine learning models commonly used for modeling spatial and time series data. A common application of GPs is regression. For example, given incomplete geographical weather data, such as temperature or humidity, how can one recover values at unobserved locations? If one…
A Case Study using Bitcoin, Ethereum, Cardano, and more.
Introduction
The Kelly criterion is a well-known strategy for sizing bets to maximize long-run expected log wealth. It is widely applied to sports betting and casino gambling. Most sources provide coverage only for the single-asset case, however. …
The definitive mathematical guide.
Ordinary least squares regression is a standard technique everyone should be familiar with. We motivate the linear model from the perspective of the Gauss-Markov Theorem, discern between the overdetermined and underdetermined cases, and apply OLS regression to a wine quality dataset.
Contents
- The Linear Model
- The Gauss Markov Theorem
- The Underdetermined…
Making the most of your compute power.
Whether you use scripting languages like Matlab and R, or high-level languages like Python, Java, or C++, optimizing for performance is a must. We review tricks for speeding up your program that every coder should know.
1. Parallelization
Parallelization is one of the most common, but oft-forgotten ways to increase the throughput…
