Markov Chains

Sergey Bryl has an introductory-level post on what Markov chains are and how they work:

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.

Related Posts

Probabilities And Poker

Steve Miller has a notebook on 5-card draw probabilities: The population of 5 card draw hands, consisting of 52 choose 5 or 2598960 elements, is pretty straightforward both mathematically and statistically. So of course ever the geek, I just had to attempt to show her how probability and statistics converge. In addition to explaining the […]

Read More

There Is No Easy Button With Predictive Analytics

Scott Mutchler dispels some myths: There are a couple of myths that I see more an more these days.  Like many myths they seem plausible on the surface but experienced data scientist know that the reality is more nuanced (and sadly requires more work). Myths: Deep learning (or Cognitive Analytics) is an easy button.  You […]

Read More

Categories

August 2016
MTWTFSS
« Jul Sep »
1234567
891011121314
15161718192021
22232425262728
293031