Introductory guide with R code included

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Image by joakant from Pixabay

Many of you might not have heard of stochastic processes before and be wondering how they might be relevant to you. Firstly, statisticians might find stochastic processes a nice way of modeling probabilistic events. Additionally, those interesting in reinforcement learning may find that this information solidifies their understanding of RL concepts such as Markov Chains. Lastly, this article is short and easy-to-follow, so if you’re curious about stochastic processes themselves, then this is a good introduction.

Any readers of the following tutorial should know matrix operations, such as multiplication, as well as a basic understanding of linear algebra. For the…


Calculate Hitting Times, Absorption Probabilities, and more

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Image by Pete Linforth from Pixabay.

If you’re unfamiliar with Stochastic processes, then I would suggest that you read my introductory article on the topic, as it covers all the background needed to understand this next tutorial. If you’re already familiar with the topic, please read on.

This tutorial focuses on using matrices to model multiple, interrelated probabilistic events. As you read this article, will learn how calculate the expected number of visits, time to reach, and probability of reaching states in a Markov chain, and a thorough mathematical explanation of the application of these techniques.

Three Heads in a Row 💰

Let’s start with a simple probability question:

Q: What is…


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Time series analysis is one of the most basic skills in a analyst’s toolkit, and it’s important for any up-and-coming data scientist to firmly grasp the concept. In this article, we’ll be going over the basic ideas behind time series analysis, and code some basic examples using NumPy. A link to the notebook containing all code and examples in this article can be found below.

What you’ll get out of this article:

>An idea of the components of time series data

>An understanding of the importance and applications of time series data analysis

>An introduction on how to do such…


Today, we’re going to learn the basic ideas behind how a neural network functions, and then go over a short example of how to construct one in Python.

Coding a neural network can be as simple as instantiating a model and then feeding it data until the desired result is produced, but it’s important to understand the underlying concepts involved in creating a neural network in order to make us of them effectively.

Typically, when we code, we create a set of rules that take in data and then provide answers. This way of coding generally works, but some problems…

Christopher Kazakis

Recent Grad, Bachelor of Arts in Computer Science and Statistics from the University of Virginia. Twitter: @chrispkazakis

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