This episode introduces the idea of a Markov Chain. A Markov Chain has a set of states describing a particular system, and a probability of moving from one state to another along every valid connected state. Markov Chains are memoryless, meaning they don't rely on a long history of previous observations. The current state of a system depends only on the previous state and the results of a random outcome.

Markov Chains are a useful way method for describing non-deterministic systems. They are useful for destribing the state and transition model of a stochastic system.

As examples of Markov Chains, we discuss stop light signals, bowling, and text prediction systems in light of whether or not they can be described with Markov Chains.

This episode explores Ordinary Least Squares or OLS - a method for finding a good fit which describes a given dataset.

The k-means clustering algorithm is an algorithm that computes a deterministic label for a given "k" number of clusters from an n-dimensional datset.  This mini-episode explores how Yoshi, our lilac crowned amazon's biological processes might be a useful way of measuring where she sits when there are no humans around.  Listen to find out how!

The Chi-Squared test is a methodology for hypothesis testing. When one has categorical data, in the form of frequency counts or observations (e.g. Vegetarian, Pescetarian, and Omnivore), split into two or more categories (e.g. Male, Female), a question may arise such as "Are women more likely than men to be vegetarian?" or put more accurately, "Is any observed difference in the frequency with which women report being vegetarian differ in a statistically significant way from the frequency men report that?"

When dealing with dynamic systems that are potentially undergoing constant change, its helpful to describe what "state" they are in.  In many applications the manner in which the state changes from one to another is not completely predictable, thus, there is uncertainty over how it transitions from state to state.  Further, in many applications, one cannot directly observe the true state, and thus we describe such situations as partially observable state spaces.  This episode explores what this means and why it is important in the context of chess, poker, and the mood of Yoshi the lilac crowned amazon parrot.

This episode introduces a high level discussion on the topic of Data Provenance, with more MINI episodes to follow to get into specific topics. Thanks to listener Sara L who wrote in to point out the Data Skeptic Podcast has focused alot about using data to be skeptical, but not necessarily being skeptical of data.

Data Provenance is the concept of knowing the full origin of your dataset. Where did it come from? Who collected it? How as it collected? Does it combine independent sources or one singular source? What are the error bounds on the way it was measured? These are just some of the questions one should ask to understand their data. After all, if the antecedent of an argument is built on dubious grounds, the consequent of the argument is equally dubious.

For a more technical discussion than what we get into in this mini epiosode, I recommend A Survey of Data Provenance Techniques by authors Simmhan, Plale, and Gannon.

In this quick holiday episode, we touch on how one would approach modeling the statistical distribution over the probability of belief in Santa Claus given age.

Love and Data is the continued theme in this mini-episode as we discuss the game theory example of The Battle of the Sexes. In this textbook example, a couple must strategize about how to spend their Friday night. One partner prefers football games while the other partner prefers to attend the opera. Yet, each person would rather be at their non-preferred location so long as they are still with their spouse. So where should they decide to go?

Economist Peter Backus put forward "The Girlfriend Equation" while working on his PhD - a probabilistic model attempting to estimate the likelihood of him finding a girlfriend. In this mini episode we explore the soundness of his model and also share some stories about how Linhda and Kyle met.

What is randomness? How can we determine if some results are randomly generated or not? Why are random numbers important to us in our everyday life? These topics and more are discussed in this mini-episode on random numbers.

Many readers will be vaguely familar with the idea of "X number of monkeys banging on Y number of typewriters for Z number of years" - the idea being that such a setup would produce random sequences of letters. The origin of this idea was the mathemetician Borel who was interested in whether or not 1,000,000 monkeys working for 10 hours per day might eventually reproduce the works of shakespeare.

We explore this topic and provide some further details in the show notes which you can find over at dataskeptic.com