These lecture notes aim at a post-Bachelor audience with a backgound at an introductory level in Applied Mathematics and Applied Statistics. They discuss the logic and methodology of the Bayes-Laplace approach to inductive statistical inference that places common sense and the guiding lines of the scientific method at the heart of systematic analyses of quantitative-empirical data. Following an exposition of exactly solvable cases of single- and two-parameter estimation, the main focus is laid on Markov Chain Monte Carlo (MCMC) simulations on the basis of Gibbs sampling and Hamiltonian Monte Carlo sampling of posterior joint probability distributions for regression parameters occurring in generalised linear models. The modelling of fixed as well as of varying effects (varying intercepts) is considered, and the simulation of posterior predictive distributions is outlined. The issues of model comparison with Bayes factors and the assessment of models’ relative posterior predictive accuracy with information entropy-based criteria DIC and WAIC are addressed. Concluding, a conceptual link to the behavioural subjective expected utility representation of a single decision-maker’s choice behaviour in static one-shot decision problems is established. Codes for MCMC simulations of multi-dimensional posterior joint probability distributions with the JAGS and Stan packages implemented in the statistical software R are provided. The lecture notes are fully hyperlinked. They direct the reader to original scientific research papers and to pertinent biographical information. An Introduction to Inductive Statistical Inference — from Parameter Estimation to Decision-Making

Retailers have been using in-store video to analyze customer behaviors and demographics for many years.  Separate systems are commonly used for different tasks.  For example, one system would count the number of customers moving through a store, in which part of the store those customers linger and near which products.  Another system will hold the store layout, whilst yet another may record transactions.  Historically, for a retailer to join these data sources to gain insights which could drive more sales by following a strategy has required complex algorithms and data structures that also require significant investment to deliver and incur ongoing maintenance costs.

Since 2015, the R Consortium has funded projects of benefit to, and proposed by, the R community. Twice a year, the R Consortium Infrastructure Steering Committee reviews grant proposals and makes awards based on merit and funds available. (Those funds come, in turn, from the annual dues paid by R Consortium members.) If you’d like to propose a project of your own, the deadline for submission for the Fall 2018 Call for Proposals is October 31.

The 4th Annual TEXATA Summit is only 2 weeks away! Join us on Friday October 19th in Austin, Texas to learn and connect with your fellow Industry Leaders discussing the latest trends and innovations in AI, Advanced Analytics, Machine Learning and Big Data.

Since 2015, the R Consortium has funded projects of benefit to, and proposed by, the R community. Twice a year, the R Consortium Infrastructure Steering Committee reviews grant proposals and makes awards based on merit and funds available. (Those funds come, in turn, from the annual dues paid by R Consortium members.) If you’d like to propose a project of your own, the deadline for submission for the Fall 2018 Call for Proposals is October 31.

Boosting with AdaBoost and Gradient Boosting

Leading the Charge 🔌 🚘: 10 Charts on Electric Vehicles in Plotly

By Harrison Jansma.

Wikistat 2.0: Educational Resources for Artificial Intelligence