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Tag: Bayesian
A First Course in Bayesian Statistical Methods (Springer Texts in Statistics)
Price:$84.99– $45.00
(as of Jan 21,2025 12:44:01 UTC – Details)
Publisher : Springer; 2009th edition (June 15, 2009)
Language : English
Hardcover : 280 pages
ISBN-10 : 0387922997
ISBN-13 : 978-0387922997
Item Weight : 2.8 pounds
Dimensions : 6.14 x 0.69 x 9.21 inchesCustomers say
Customers find the book provides a good introduction to Bayesian analysis concepts with clear explanations and examples. They describe the content as excellent, thorough, and motivating. The R code provided is nice. However, some readers feel the book is not an easy introduction and not suitable for first-year courses. There are mixed opinions on the clarity of the text, with some finding it clear and easy to read, while others report errors and typos.
AI-generated from the text of customer reviews
Are you looking to dive into the world of Bayesian statistical methods? Look no further than “A First Course in Bayesian Statistical Methods” from the Springer Texts in Statistics series. This comprehensive and accessible textbook covers all the essential topics in Bayesian statistics, from basic concepts to advanced techniques.Whether you are a beginner or an experienced statistician, this book will provide you with a solid foundation in Bayesian methods. With clear explanations, numerous examples, and exercises to reinforce your understanding, this book is perfect for self-study or as a textbook for a course in Bayesian statistics.
Don’t miss out on the opportunity to learn from one of the leading experts in the field. Get your copy of “A First Course in Bayesian Statistical Methods” today and start mastering Bayesian statistics!
#Bayesian #Statistical #Methods #Springer #Texts #Statistics,machine learning: an applied mathematics introductionReasoning with Data: An Introduction to Traditional and Bayesian Statistics Using R
Price: $42.27
(as of Jan 19,2025 03:22:41 UTC – Details)
ASIN : B0718ZX97T
Publisher : The Guilford Press; Annotated edition (April 28, 2017)
Publication date : April 28, 2017
Language : English
File size : 8683 KB
Text-to-Speech : Not enabled
Enhanced typesetting : Not Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 325 pages
Format : Print ReplicaCustomers say
Customers find the book provides a refreshing introduction to data science using R. They appreciate the clear writing style and straightforward presentation of complex topics. The book is easy for them to use, with simple workarounds for outdated scripts and intuitive software like R.
AI-generated from the text of customer reviews
Reasoning with Data: An Introduction to Traditional and Bayesian Statistics Using RIn today’s data-driven world, the ability to reason with data is more important than ever. Traditional statistics has long been the go-to method for analyzing data and making inferences about populations. However, in recent years, Bayesian statistics has gained popularity as a more flexible and intuitive approach to data analysis.
In this post, we will provide an introduction to both traditional and Bayesian statistics using the popular statistical software R. We will cover the basic principles of each approach, as well as how to implement them using R.
Traditional statistics involves using methods such as hypothesis testing, confidence intervals, and regression analysis to make inferences about a population based on a sample of data. These methods are based on the frequentist interpretation of probability, which views probability as the long-run frequency of an event occurring.
On the other hand, Bayesian statistics takes a different approach by viewing probability as a measure of uncertainty or belief. In Bayesian statistics, we update our beliefs about a population based on both prior knowledge and new data. This allows for more flexibility in modeling complex relationships and incorporating subjective information into our analysis.
Using R, we can easily implement both traditional and Bayesian statistical methods. R provides a wide range of packages for performing statistical analysis, including base R functions for traditional statistics and packages such as Stan and JAGS for Bayesian analysis.
By learning how to reason with data using both traditional and Bayesian statistics in R, you will be better equipped to analyze and interpret data in a meaningful way. Whether you are a beginner or an experienced data analyst, understanding these statistical methods will enhance your ability to make informed decisions based on data.
Stay tuned for future posts where we will dive deeper into specific statistical techniques and examples of how to implement them in R. Reasoning with data is a powerful skill that can help you unlock valuable insights and drive informed decision-making in your work and research.
#Reasoning #Data #Introduction #Traditional #Bayesian #Statistics,machine learning: an applied mathematics introductionBayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data and Analytics)
Price: $39.99
(as of Jan 18,2025 01:35:55 UTC – Details)
Publisher : Addison-Wesley Professional; 1st edition (October 2, 2015)
Language : English
Paperback : 256 pages
ISBN-10 : 0133902838
ISBN-13 : 978-0133902839
Item Weight : 12.5 ounces
Dimensions : 6.9 x 0.5 x 8.9 inchesCustomers say
Customers find the book informative and practical for learning about programming from a Bayesian perspective. They describe it as a useful reference that helps them learn concepts of modeling and Bayesian programming. The content is well-written and easy to understand, making it a compelling read.
AI-generated from the text of customer reviews
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data and Analytics)Are you interested in learning about Bayesian methods and how they can be applied to solve real-world problems? Look no further than “Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference” by Cam Davidson-Pilon. This book is a comprehensive guide to understanding Bayesian statistics and probabilistic programming, aimed at hackers and data scientists looking to take their skills to the next level.
With a focus on practical applications and hands-on examples, this book will teach you how to use probabilistic programming languages like PyMC3 to build Bayesian models and make probabilistic predictions. You’ll learn how to perform Bayesian inference, simulate data, and interpret the results of your analyses. Whether you’re new to Bayesian methods or looking to deepen your understanding, this book is a valuable resource for anyone interested in using Bayesian statistics in their work.
Don’t miss out on the opportunity to master Bayesian methods and elevate your data analysis skills. Pick up a copy of “Bayesian Methods for Hackers” today and start unlocking the power of probabilistic programming and Bayesian inference.
#Bayesian #Methods #Hackers #Probabilistic #Programming #Bayesian #Inference #AddisonWesley #Data #Analytics,machine learning: an applied mathematics introductionReasoning with Data: An Introduction to Traditional and Bayesian Statistics Using R
Price: $52.35
(as of Jan 17,2025 03:54:34 UTC – Details)
ASIN : 1462530265
Publisher : The Guilford Press; Annotated edition (May 22, 2017)
Language : English
Paperback : 325 pages
ISBN-10 : 9781462530267
ISBN-13 : 978-1462530267
Item Weight : 1.28 pounds
Dimensions : 7 x 0.55 x 10 inchesCustomers say
Customers find the book provides a refreshing introduction to data science using R. They appreciate the clear writing style and straightforward presentation of complex topics. The book is easy for them to use, with simple workarounds for outdated scripts and intuitive software like R.
AI-generated from the text of customer reviews
Reasoning with Data: An Introduction to Traditional and Bayesian Statistics Using RAre you interested in learning how to analyze data and make informed decisions using statistical methods? If so, you’ve come to the right place. In this post, we will introduce you to the world of traditional and Bayesian statistics using the popular programming language R.
Statistics is the science of collecting, analyzing, and interpreting data in order to make decisions based on evidence. Traditional statistics, also known as frequentist statistics, is the most commonly used approach in statistical analysis. It involves making inferences about a population based on a sample of data.
On the other hand, Bayesian statistics is a more modern approach that uses probability theory to update beliefs about the world as new evidence is collected. This approach is gaining popularity in many fields, including machine learning, finance, and healthcare.
In this post, we will show you how to use R, a powerful and versatile programming language for statistical analysis, to perform traditional and Bayesian statistics. We will cover topics such as hypothesis testing, regression analysis, and Bayesian inference.
By the end of this post, you will have a solid understanding of the basics of traditional and Bayesian statistics and how to apply them using R. So, if you’re ready to reason with data and make better decisions, keep reading.
#Reasoning #Data #Introduction #Traditional #Bayesian #Statistics,machine learning: an applied mathematics introductionBayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
Price:$39.99– $27.93
(as of Jan 17,2025 01:39:10 UTC – Details)
From the Publisher
‘Excellent’
“An excellent introduction to subjects critical to all data scientists.”
—insideBIGDATA
‘Engaging’
“An engaging introduction . . . the book introduces Bayesian methods in a clear and concise manner, without assuming prior statistical knowledge and, for the most part, eschewing formulations. . . . quite suitable for a crash course in applied Bayesian statistics.”
—Jose D. Perezgonzalez, Frontiers in Psychology
‘Fantastic’
“This is one of the best (and only) introductory statistics books I’ve seen . . . it’s a fantastic read.”
—Chelsea Parlett-Pelleriti, Technical Reviewer
About the Author
Will Kurt currently works as a data scientist at Wayfair, and has been using Bayesian statistics to solve real business problems for over half a decade. He frequently blogs about probability on his website, CountBayesie.com. Kurt is the author of Get Programming with Haskell (Manning Publications) and lives in Boston, Massachusetts.
Who Should Read This Book
The only requirement of this book is basic high school algebra. If you flip forward, you’ll see a few instances of math, but nothing particularly onerous. We’ll be using a bit of code written in the R programming language, which I’ll provide and talk through, so there’s no need to have learned R beforehand. We’ll also touch on calculus, but again no prior experience is required, and the appendixes will give you enough information to cover what you’ll need.
About the Publisher
No Starch Press has published the finest in geek entertainment since 1994, creating both timely and timeless titles like Python Crash Course, Python for Kids, How Linux Works, and Hacking: The Art of Exploitation. An independent, San Francisco-based publishing company, No Starch Press focuses on a curated list of well-crafted books that make a difference. They publish on many topics, including computer programming, cybersecurity, operating systems, and LEGO. The titles have personality, the authors are passionate experts, and all the content goes through extensive editorial and technical reviews. Long known for its fun, fearless approach to technology, No Starch Press has earned wide support from STEM enthusiasts worldwide.
Publisher : No Starch Press; Illustrated edition (July 9, 2019)
Language : English
Paperback : 256 pages
ISBN-10 : 1593279566
ISBN-13 : 978-1593279561
Item Weight : 1.1 pounds
Dimensions : 7.06 x 0.65 x 9.25 inchesCustomers say
Customers find the book provides a good introduction to Bayesian statistics using examples to clarify key concepts. They describe it as an enjoyable, entertaining read that helps develop an understanding of the topics. The text is clear and easy to understand, with fluid writing that doesn’t get too wordy.
AI-generated from the text of customer reviews
Are you tired of boring statistics textbooks and confusing probability problems? Fear not, because Bayesian Statistics can be fun and engaging with a little help from Star Wars, LEGO, and rubber ducks!In this post, we will explore the world of Bayesian Statistics through the lens of our favorite intergalactic saga, Star Wars. Imagine using the Force to predict the probability of your favorite character winning a lightsaber duel, or using Bayesian inference to estimate the odds of the Empire catching the Rebel Alliance.
But that’s not all – we will also dive into the world of LEGO, where building blocks represent different data points and probabilities. You can create your own LEGO models to visualize complex statistical concepts and test hypotheses in a hands-on, interactive way.
And let’s not forget about the rubber ducks – a classic tool for teaching probability and decision-making. By conducting experiments with rubber ducks, you can learn about the principles of Bayesian reasoning and make informed predictions about future outcomes.
So grab your lightsaber, gather your LEGO bricks, and line up your rubber ducks – it’s time to make statistics and probability fun with Bayesian Statistics the fun way! May the Force be with you as you embark on this statistical adventure.
#Bayesian #Statistics #Fun #Understanding #Statistics #Probability #Star #Wars #LEGO #Rubber #Ducks,machine learning: an applied mathematics introduction
Bayes Rules!: An Introduction to Applied Bayesian Modeling (Chapman & Hall/CRC Texts in Statistical Science)
Price: $81.33
(as of Dec 28,2024 06:52:15 UTC – Details)
Publisher : Chapman and Hall/CRC; 1st edition (March 4, 2022)
Language : English
Paperback : 544 pages
ISBN-10 : 0367255391
ISBN-13 : 978-0367255398
Item Weight : 2.49 pounds
Dimensions : 10 x 7.01 x 1.13 inches
Are you interested in learning about Bayesian modeling and how it can be applied in various fields? Look no further than “Bayes Rules!: An Introduction to Applied Bayesian Modeling,” a comprehensive text from Chapman & Hall/CRC Texts in Statistical Science.In this book, readers will discover the fundamentals of Bayesian statistics and how it can be used to make informed decisions in a variety of real-world scenarios. From healthcare to finance to marketing, Bayesian modeling offers a powerful tool for analyzing data and making predictions.
Written by experts in the field, “Bayes Rules!” provides a clear and accessible introduction to Bayesian modeling, making it suitable for both beginners and experienced statisticians. Whether you’re looking to expand your knowledge or dive into a new area of statistical analysis, this book is a must-read.
So why wait? Pick up “Bayes Rules!: An Introduction to Applied Bayesian Modeling” today and start mastering the art of Bayesian statistics!
#Bayes #Rules #Introduction #Applied #Bayesian #Modeling #Chapman #HallCRC #Texts #Statistical #Science
Machine Learning in Geomechanics 2: Data-Driven Modeling, Bayesian Inference, Ph
Machine Learning in Geomechanics 2: Data-Driven Modeling, Bayesian Inference, Ph
Price : 171.39
Ends on : N/A
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ysical InterpretationIn our last post, we discussed the basics of machine learning in geomechanics and how it can be applied to predict rock properties and behavior. In this post, we will delve deeper into data-driven modeling, Bayesian inference, and the physical interpretation of machine learning models in geomechanics.
Data-driven modeling is a powerful tool in geomechanics that allows us to build predictive models based on large datasets of rock properties, stress conditions, and other relevant variables. By using machine learning algorithms such as neural networks, support vector machines, or decision trees, we can analyze complex relationships between these variables and make accurate predictions about rock behavior under various conditions.
Bayesian inference is another important concept in machine learning that can be applied to geomechanics. This statistical method allows us to update our beliefs about rock properties and behavior based on new data or evidence. By incorporating prior knowledge and uncertainty into our models, we can make more robust predictions and improve the reliability of our geomechanical analyses.
Finally, the physical interpretation of machine learning models is crucial in geomechanics to ensure that the predictions are meaningful and can be used to inform engineering decisions. By understanding the underlying mechanisms that drive rock behavior, we can interpret the results of our machine learning models in a way that is consistent with established geomechanical principles.
Overall, machine learning has the potential to revolutionize the field of geomechanics by providing new insights into rock properties and behavior. By combining data-driven modeling, Bayesian inference, and physical interpretation, we can develop more accurate and reliable predictive models that can be used to optimize engineering design and mitigate risks in geomechanical projects.
#Machine #Learning #Geomechanics #DataDriven #Modeling #Bayesian #Inference,machine learning
Bayesian Sports Models in R for Beginner’s: Step-by-Step Techniques and Real-World Applications for Predictive Analytics in Sports
Price: $47.99
(as of Dec 27,2024 03:43:09 UTC – Details)
ASIN : B0DFXK8NSV
Publication date : September 2, 2024
Language : English
File size : 1492 KB
Simultaneous device usage : Unlimited
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 168 pages
Bayesian Sports Models in R for Beginner’s: Step-by-Step Techniques and Real-World Applications for Predictive Analytics in SportsAre you interested in using advanced statistical techniques to predict outcomes in sports? Bayesian sports models are a powerful tool that can help you make more accurate predictions and gain insights into the underlying factors that influence game outcomes.
In this post, we will provide a step-by-step guide for beginners on how to build Bayesian sports models using the R programming language. We will cover the basics of Bayesian statistics, how to set up a model, and how to interpret the results.
We will also discuss real-world applications of Bayesian sports models in predicting game outcomes, player performance, and more. Whether you are a sports fan looking to gain a competitive edge in fantasy sports or a data scientist interested in applying predictive analytics to sports, this post will provide you with the knowledge and tools you need to get started.
Stay tuned for our upcoming posts where we will dive deeper into specific sports and demonstrate how Bayesian models can be used to make accurate predictions in basketball, football, soccer, and more. Don’t miss out on this exciting opportunity to level up your sports analytics game with Bayesian sports models in R!
#Bayesian #Sports #Models #Beginners #StepbyStep #Techniques #RealWorld #Applications #Predictive #Analytics #Sports
Bayesian Reasoning and Machine Learning – Hardcover, by Barber David – Very Good
Bayesian Reasoning and Machine Learning – Hardcover, by Barber David – Very Good
Price : 69.34
Ends on : N/A
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I recently picked up a copy of “Bayesian Reasoning and Machine Learning” by David Barber, and I must say, I am thoroughly impressed. This hardcover edition is in excellent condition and is a valuable addition to my collection of books on machine learning.Barber’s book delves deep into the principles of Bayesian reasoning and how it can be applied to the field of machine learning. The author’s clear and concise writing style makes complex concepts easy to understand, making this book a must-read for anyone looking to expand their knowledge in this area.
Whether you are a beginner looking to learn the basics of Bayesian reasoning or an experienced practitioner wanting to deepen your understanding, this book is sure to be a valuable resource. I highly recommend “Bayesian Reasoning and Machine Learning” by David Barber to anyone interested in the intersection of these two fascinating fields.
#Bayesian #Reasoning #Machine #Learning #Hardcover #Barber #David #Good
Machine Learning: A Bayesian and Optimization Perspective
Price:$105.00– $72.45
(as of Dec 26,2024 12:54:50 UTC – Details)
Publisher : Academic Press; 2nd edition (July 31, 2020)
Language : English
Hardcover : 1160 pages
ISBN-10 : 0128188030
ISBN-13 : 978-0128188033
Item Weight : 5.4 pounds
Dimensions : 7.5 x 2.25 x 9.25 inches
Machine Learning: A Bayesian and Optimization PerspectiveIn the world of machine learning, there are various approaches and techniques that can be used to build predictive models and make sense of complex data. Two key perspectives that are often used in machine learning are Bayesian inference and optimization.
Bayesian inference is a statistical method that uses probability theory to update beliefs about the parameters of a model based on new evidence or data. By treating model parameters as random variables and incorporating prior knowledge, Bayesian inference allows for more robust and interpretable modeling.
On the other hand, optimization techniques are used to find the best set of parameters for a given model by minimizing a loss function. This involves iteratively updating model parameters to improve the model’s performance on a given task, such as classification or regression.
By combining Bayesian inference with optimization techniques, machine learning practitioners can build more accurate and efficient models. Bayesian optimization, for example, is a popular approach that uses Bayesian inference to model the uncertainty in the relationship between model parameters and the objective function, and optimization techniques to find the best set of parameters.
Overall, a Bayesian and optimization perspective in machine learning can lead to more reliable and interpretable models that can make better predictions and decisions. By understanding and incorporating these perspectives into their workflow, machine learning practitioners can unlock the full potential of their data and build more powerful predictive models.
#Machine #Learning #Bayesian #Optimization #Perspective
