## Bayes Theorem Examples

Author: Scott Hartshorn
Publisher: Lulu Press, Inc
ISBN: 1329854128
Format: PDF, ePub

Bayes theorem describes the probability of an event based on other information that might be relevant. Essentially, you are estimating a probability, but then updating that estimate based on other things that you know. This book is designed to give you an intuitive understanding of how to use Bayes Theorem. It starts with the definition of what Bayes Theorem is, but the focus of the book is on providing examples that you can follow and duplicate. Most of the examples are calculated in Excel, which is useful for updating probability if you have dozens or hundreds of data points to roll in.

## Bayes Theorem Examples a Visual Introduction for Beginners

Author: Dan Morris
Publisher:
ISBN: 9781549761744
Format: PDF

## Bayes Theorem Examples

Author: Logan Styles
Publisher: Createspace Independent Publishing Platform
ISBN: 9781535194594
Format: PDF, ePub

## A User s Guide to Business Analytics

Author: Ayanendranath Basu
Publisher: CRC Press
ISBN: 1466591668
Format: PDF, ePub, Mobi

A User's Guide to Business Analytics provides a comprehensive discussion of statistical methods useful to the business analyst. Methods are developed from a fairly basic level to accommodate readers who have limited training in the theory of statistics. A substantial number of case studies and numerical illustrations using the R-software package are provided for the benefit of motivated beginners who want to get a head start in analytics as well as for experts on the job who will benefit by using this text as a reference book. The book is comprised of 12 chapters. The first chapter focuses on business analytics, along with its emergence and application, and sets up a context for the whole book. The next three chapters introduce R and provide a comprehensive discussion on descriptive analytics, including numerical data summarization and visual analytics. Chapters five through seven discuss set theory, definitions and counting rules, probability, random variables, and probability distributions, with a number of business scenario examples. These chapters lay down the foundation for predictive analytics and model building. Chapter eight deals with statistical inference and discusses the most common testing procedures. Chapters nine through twelve deal entirely with predictive analytics. The chapter on regression is quite extensive, dealing with model development and model complexity from a user’s perspective. A short chapter on tree-based methods puts forth the main application areas succinctly. The chapter on data mining is a good introduction to the most common machine learning algorithms. The last chapter highlights the role of different time series models in analytics. In all the chapters, the authors showcase a number of examples and case studies and provide guidelines to users in the analytics field.

## Neuronale Netze selbst programmieren

Author: Tariq Rashid
Publisher: O'Reilly
ISBN: 3960101031
Format: PDF, ePub

Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Sie sind Grundlage vieler Anwendungen im Alltag wie beispielsweise Spracherkennung, Gesichtserkennung auf Fotos oder die Umwandlung von Sprache in Text. Dennoch verstehen nur wenige, wie neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie neuronale Netze arbeiten: - Zunächst lernen Sie die mathematischen Konzepte kennen, die den neuronalen Netzen zugrunde liegen. Dafür brauchen Sie keine tieferen Mathematikkenntnisse, denn alle mathematischen Ideen werden behutsam und mit vielen Illustrationen und Beispielen erläutert. Eine Kurzeinführung in die Analysis unterstützt Sie dabei. - Dann geht es in die Praxis: Nach einer Einführung in die populäre und leicht zu lernende Programmiersprache Python bauen Sie allmählich Ihr eigenes neuronales Netz mit Python auf. Sie bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. - Im nächsten Schritt tunen Sie die Leistung Ihres neuronalen Netzes so weit, dass es eine Zahlenerkennung von 98 % erreicht – nur mit einfachen Ideen und simplem Code. Sie testen das Netz mit Ihrer eigenen Handschrift und werfen noch einen Blick in das mysteriöse Innere eines neuronalen Netzes. - Zum Schluss lassen Sie das neuronale Netz auf einem Raspberry Pi Zero laufen. Tariq Rashid erklärt diese schwierige Materie außergewöhnlich klar und verständlich, dadurch werden neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.

## A Student s Guide to Bayesian Statistics

Author: Ben Lambert
Publisher: SAGE
ISBN: 1526418266
Format: PDF, ePub

Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inference Understanding Bayes' rule Nuts and bolts of Bayesian analytic methods Computational Bayes and real-world Bayesian analysis Regression analysis and hierarchical methods This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses.

## The Blackwell Guide to the Philosophy of Science

Author: Peter Machamer
Publisher: John Wiley & Sons
ISBN: 0470756527
Format: PDF, ePub, Mobi

This volume presentsa definitive introduction to the core areas of philosophy of science.

## Der Zahlensinn oder Warum wir rechnen k nnen

Author: Stanislas Dehaene
Publisher: Springer-Verlag
ISBN: 3034878257
Format: PDF

Wir sind umgeben von Zahlen. Ob auf Kreditkarten gestanzt oder auf Münzen geprägt, ob auf Schecks gedruckt oder in den Spalten computerisierter Tabellen aufgelistet, überall beherrschen Zahlen unser Leben. Sie sind auch der Kern unserer Technologie. Ohne Zahlen könnten wir weder Raketen starten, die das Sonnensystem erkunden, noch Brücken bauen, Güter austauschen oder Rech nungen bezahlen. In gewissem Sinn sind Zahlen also kulturelle Erfindungen, die sich ihrer Bedeutung nach nur mit der Landwirtschaft oder mit dem Rad vergleichen lassen. Aber sie könnten sogar noch tiefere Wurzeln haben. Tausende von Jahren vor Christus benutzten babylonische Wissenschaftler Zahlzeichen, um erstaun lich genaueastronomische Tabellen zu berechnen. Zehntausende von Jahren zuvor hatten Menschen der Steinzeit die ersten geschriebenen Zahlenreihen geschaffen, indem sie Knochen einkerbten oder Punkte auf Höhlenwände malten. Und, wie ich später überzeugend darzustellen hoffe, schon vor weiteren Millionen von Jahren, lange bevor es Menschen gab, nahmen Tiere aller Arten Zahlen zur Kenntnis und stellten mit ihnen einfache Kopfrechnungen an. Sind Zahlen also fast so alt wie das Leben selbst? Sind sie in der Struktur unseres Gehirns verankert? Besitzen wir einen Zahlensinn, eine spezielle Intuition, die uns hilft, Zahlen und Mathematik mit Sinn zu erfüllen? Ich wurde vor fünfzehn Jahren, während meiner Ausbildung zum Mathema tiker, fasziniert von den abstrakten Objekten, mit denen ich umzugehen lernte, vor allem von den einfachsten von ihnen- den Zahlen.

## Bayes Theorem Bayes Theorem Examples

Author: K. A. Muhith
Publisher: Createspace Independent Publishing Platform
ISBN: 9781540675132
Format: PDF, ePub, Docs

Bayes Theorem: Bayes Theorem Examples: A Step by Step Guide For Beginners This book describes Bayes' Theorem in the simplest way possible in layman's language. This book does not contain complex formulas, jargon and hard-to-understand mathematical processes. Some unique examples like US presidential election (Trump vs Hilary!), picnic day, job interview, accident eyewitness reliability, breathalyzer test has been described and solved elaborately in step by step manner in this booklet. After going through this book, you will have a better understanding on what Bayes' Theorem is, when & where you can apply it and how it can help you deduce the most logical conclusions. From Amazon search results to dating, from spam filtering from email to search & rescue, from finding who might win the election to finding a missing plane - Bayes' has been used in real life scenario for many years. Bayes' Theorem is part of syllabus for science and business students. It is one the most basic theorems of statistics. But most teaching techniques of this theorem is too much analytical. Very little intuitive and visual aids is used to demonstrate this theorem in practice. This makes it very hard to grasp the core concept of Bayes' theorem and understand the applicability. This guide addresses this issue and introduces some visual examples and step by step guidelines to solve real life problems. There easy to follow steps will help you apply Bayes' theorem quickly in real life.

## Bayes Theorem

Author: Arthur Taff
Publisher: Createspace Independent Publishing Platform
ISBN: 9781984178947
Format: PDF, Mobi

The Perfect Book for Beginners Wanting to Visually Learn About Bayes Theorem Through Real Examples! What if you could quickly and easily learn Bayesian data analysis without complex textbooks and statistics classes? Imagine being able to apply your newly learned theory to real life situations! Multi-time best selling IT & mathematics author, Arthur Taff, presents the perfect guide for any beginner. Bayesian data analysis can be difficult to learn, especially through textbooks and statistic classes at school. This book aims to solve that issue by presenting the theories in an easy-to-understand and visually intuitive way. This book contains a number of visual examples to build a basic understanding of Bayesian data analysis and then works to teach at a deeper level without the complexities you'd see in other similar books. Additionally, every example in this book has been solved using Excel. In this book, you will get: A Basic Introduction to Bayes Theorem (with examples) - The initial introduction demonstrates how Bayesian data analysis works when you have a single new piece of data to update initial probabilities. Adding New Data & Updating Probabilities - Takes the above example and looks at what happens if we have multiple pieces of data instead of a single piece. Bayes Theorem Terminology - The formal names for the different parts of the Bayes Theorem equation, and how it all comes together for an easier overall understanding. How to Deal With Data Errors - In a real life situation, it is unlikely that your data will be error-free. This section shows you how to deal with those errors and still get accurate probability estimates. Arthur's personal email address for unlimited customer support if you have any questions And much, much more... If you are a person that learns by example, especially visually, then this book is perfect for you! It is a very important topic in a wide range of industries - so dive in to get a deep understanding!