### How Mathematical Discoveries are Made

In one of my previous articles, you can learn the process about how discoveries are made by research scientists, from exploratory analysis, testing, simulations, data science guesswork, all the way to the discovery of a new theory and state-of-the-art statistical modeling,including new, fundamental mathematical/statistical equations.

### How Mathematical Discoveries are Made

In one of my previous articles, you can learn the process about how discoveries are made by research scientists, from stating the problem, exploratory analysis, testing, simulations, data science guesswork, all the way to the discovery of a new theory and state-of-the-art statistical modeling,including new, fundamental mathematical/statistical equations.

### Sling adds Discovery, Science to its lineup

Sling TV's line up of available channels is getting bigger. The streaming TV service is adding nine new channels from Discovery Networks that offer live and on-demand content, including the flagship Discovery Channel and MotorTrend. The best news for Sling subscribers: some of the channels will be added to your package for free. Access to the channels will be split across Sling's two separate service packages, both of which cost \$25 per month. Sling Blue will get Discovery Channel, Investigation Discovery and TLC.

### Local Causal Discovery of Direct Causes and Effects

We focus on the discovery and identification of direct causes and effects of a target variable in a causal network. State-of-the-art algorithms generally need to find the global causal structures in the form of complete partial directed acyclic graphs in order to identify the direct causes and effects of a target variable. While these algorithms are effective, it is often unnecessary and wasteful to find the global structures when we are only interested in one target variable (such as class labels). We propose a new local causal discovery algorithm, called Causal Markov Blanket (CMB), to identify the direct causes and effects of a target variable based on Markov Blanket Discovery. CMB is designed to conduct causal discovery among multiple variables, but focuses only on finding causal relationships between a specific target variable and other variables.

### Stylized innovation: interrogating incrementally available randomised dictionaries

Inspired by recent work of Fink, Reeves, Palma and Farr (2017) on innovation in language, gastronomy, and technology, I study how new symbol discovery manifests itself in terms of additional "word" vocabulary being available from dictionaries generated from a finite number of symbols. Several distinct dictionary generation models are investigated using numerical simulation, with emphasis on the scaling of knowledge as dictionary generators and parameters are varied, and the role of which order the symbols are discovered in.