Introduction Apache Spark is a Scala library for analysing "big data". It can be used for analysing huge (internet-scale) datasets distributed across large clusters of machines. The analysis can be anything from the computation of simple descriptive statistics associated with the datasets, through to rather sophisticated machine learning pipelines involving data pre-processing, transformation, nonlinear model … Continue reading A quick introduction to Apache Spark for statisticians
Introduction People regularly ask me about books and other resources for getting started with Scala for statistical computing and data science. This post will focus on books, but it’s worth briefly noting that there are a number of other resources available, on-line and otherwise, that are also worth considering. I particularly like the Coursera course … Continue reading Books on Scala for statistical computing and data science
This post will review the book: Scala for Data Science, Bugnion, Packt, 2016. Disclaimer: This book review has not been solicited by the publisher (or anyone else) in any way. I purchased the review copy of this book myself. I have not received any benefit from the writing of this review. Introduction On this blog … Continue reading Scala for Data Science [book review]
Introduction In the previous post I gave a quick introduction to some important concepts in functional programming, such as HOFs, closures, currying and partial application, and hopefully gave some insight into why these concepts might be useful in the context of scientific computing. Another concept that is very important in modern functional programming is that … Continue reading First steps with monads in Scala
This blog post is derived from a computer practical session that I ran as part of my new course on Statistics for Big Data, previously discussed. This course covered a lot of material very quickly. In particular, I deferred introducing notions of hierarchical modelling until the Bayesian part of the course, where I feel it … Continue reading One-way ANOVA with fixed and random effects from a Bayesian perspective
This blog post is derived from a computer practical session that I ran as part of my new course on Statistics for Big Data, previously discussed. This course covered a lot of material very quickly. In particular, I deferred introducing notions of hierarchical modelling until the Bayesian part of the course, where I feel it … Continue reading One-way ANOVA with fixed and random effects from a Bayesian perspective
Hats off for Martin Šmíra, who has finished porting the models from Michael Lee and Eric-Jan Wagenmakers’ book Bayesian Cognitive Modeling to Stan. Here they are: Bayesian Cognitive Modeling: Stan Example Models Martin managed to port 54 of the 57 models in the book and verified that the Stan code got the same answers as […] The post Bayesian Cognitive Modeling Models Ported to Stan appeared first on Statistical Modeling,…
Enthought Canopy is a comprehensive Python analysis environment with easy installation and updates of the proven Enthought Python distribution - all part of a robust platform you can explore, develop and visualize on. (Ref. 1)To install this, do the f...
According to the central limit theorem, if random variables, , are independent and identically distributed, is sufficiently large, then the distribution of their sample mean, , is approximately normal, and this approximation is better as increases. One of the most remarkable aspects of the central limit theorem (CLT) is its validity for any parent distribution of […]
It's been two weeks for search and rescue operations of the Malaysia Airlines Flight MH370, after it vanished from the radar on March 8, 2014. And wherever they are, we hope and pray for them.Photo from VENUS - Wall of Hope & Prayers for MH370In th...
