#### Overview of this book

There are many algorithms for data analysis and it’s not always possible to quickly choose the best one for each case. Implementation of the algorithms takes a lot of time. With the help of Mathematica, you can quickly get a result from the use of a particular method, because this system contains almost all the known algorithms for data analysis. If you are not a programmer but you need to analyze data, this book will show you the capabilities of Mathematica when just few strings of intelligible code help to solve huge tasks from statistical issues to pattern recognition. If you're a programmer, with the help of this book, you will learn how to use the library of algorithms implemented in Mathematica in your programs, as well as how to write algorithm testing procedure. With each chapter, you'll be more immersed in the special world of Mathematica. Along with intuitive queries for data processing, we will highlight the nuances and features of this system, allowing you to build effective analysis systems. With the help of this book, you will learn how to optimize the computations by combining your libraries with the Mathematica kernel.
Mathematica Data Analysis
Credits
www.PacktPub.com
Preface
Free Chapter
First Steps in Data Analysis
Creating an Interface for an External Program
Analyzing Data with the Help of Mathematica
Discovering the Advanced Capabilities of Time Series
Statistical Hypothesis Testing in Two Clicks
Predicting the Dataset Behavior
Rock-Paper-Scissors – Intelligent Processing of Datasets
Index

## Probability automaton modelling

In order to predict the behavior of more complex systems that are affected by random factors, you can use modeling. For example, the probability automaton modeling method allows us to make a model in the form of interconnected automatons whose states are changing in time simultaneously and discretely. The automaton receives some input signal, has an internal state that includes the state generated by a random value, and is capable of producing an output signal.

Let's consider a simple model of an ATM and its implementation in Mathematica. Let's assume that there is an ATM, which is replenished every `t` interval by a constant `r` value. The ATM is approached at random intervals by customers, who withdraw a random amount of money. If there is no money, the ATM receives a negative feedback with a value, `v`, and to deliver the money, you need to spend certain variables. At the same time, the bank receives income for every unit of time that amounts to an α percent of...