# Markov Chains

2016-08-05

Using Markov chains allow us to switch from heuristic models to probabilistic ones. We can represent every customer journey (sequence of channels/touchpoints) as a chain in a directed Markov graph where each vertex is a possible state (channel/touchpoint) and the edges represent the probability of transition between the states (including conversion.) By computing the model and estimating transition probabilities we can attribute every channel/touchpoint.

Let’s start with a simple example of the first-order or “memory-free” Markov graph for better understanding the concept. It is called “memory-free” because the probability of reaching one state depends only on the previous state visited.

Markov chains are great for behavior prediction and sentence formation.  This is part one of a series I will eagerly anticipate.  H/T R Bloggers.

## Reviewing Word Associations With R

2018-12-18

Julia Silge does some exploratory analysis on the Small World of Words project: The Small World of Words project focuses on word associations. You can try it out for yourself to see how it works, but the general idea is that the participant is presented with a word (from “telephone” to “journalist” to “yoga”) and is then […]

## Using ggplot And plotly To Visualize Multivariate Data

2018-12-18

Sebastian Sauer shows us a few techniques for visualizing multivariate data, using ggplot2 in some cases and plotly in others: Plotting univariate (sampled) normal dataWell, that’s obvious.d %>% ggplot(aes(x = X1)) + geom_density() It gets much less obvious from there.  It was also interesting learning about ggplotly, a function which translates ggplot2 visuals to plotly visuals.