Title: Think Bayes BY Allen B. Downey
Posted by:Allen B. Downey
Published :2019-04-09T04:52:49+00:00
About Allen B. Downey
Allen B. Downey Is a well-known author, some of his books are a fascination for readers like in the Think Bayes book, this is one of the most wanted Allen B. Downey author readers around the world.
379 Replys to “Think Bayes”
very good Bayesian introduction, specially because it's light on mathematics and full of practical content. I searched for this kind of content for a long time, but was surprised to find in a book like this.
Science has been described as simply “a collection of successful recipes”. In “Think Bayes” Allen B. Downey has attempted just that by presenting a set of instructional tutorials for teaching bayesian methods with Python. In essence it’s an instructional book with examples that are meant to be straightforward by giving you a simple set of rules in solving more complex sets of problems. The book also makes a few style choices, ignoring continuous distributions in an effort to focus on d [...]
This book is great in term of providing wide range of examples and exercises by those we can understand more about how to "think bayes". However, there are still lacks of detail explaination , and mixture of python code and math is not making it easier to understand.
While the methodology behind the framework of the code examples wasn't always obvious (and seemed occasionally overwrought), I think the core statistical concepts come through clearly enough that they could be reimplemented in whatever fashion made most sense to the reader. Generally fairly concise, and generous with graphical outputs as well, which helped solidify conceptual aspects of distributions and their properties.
Good introductory book with interesting example problems. The example code layers abstractions on top of the previously introduced ones from chapter to chapter. Over time it gets hard to comprehend the examples due to class-based polymorphism with multiple levels of inheritance. If not this annoyance, it would be a great book.
Good introduction to Bayesian analysis. I didn't take the time this time through to do all of the code samples and exercises, but I still got a decent overview. One of the best parts was the first really good explanation of the Monty Hall problem that I've seen; I finally understand it!
I'm not giving this up because I didn't find it interesting. I'm putting it on hold because there are some technical books that I need to read first (for work purposes.)
Allen B. Downey Is a well-known author, some of his books are a fascination for readers like in theThink Bayes book, this is one of the most wanted Allen B. Downey author readers around the world.
379 Replys to “Think Bayes”
very good Bayesian introduction, specially because it's light on mathematics and full of practical content. I searched for this kind of content for a long time, but was surprised to find in a book like this.
Science has been described as simply “a collection of successful recipes”. In “Think Bayes” Allen B. Downey has attempted just that by presenting a set of instructional tutorials for teaching bayesian methods with Python. In essence it’s an instructional book with examples that are meant to be straightforward by giving you a simple set of rules in solving more complex sets of problems. The book also makes a few style choices, ignoring continuous distributions in an effort to focus on d [...]
This book is great in term of providing wide range of examples and exercises by those we can understand more about how to "think bayes". However, there are still lacks of detail explaination , and mixture of python code and math is not making it easier to understand.
While the methodology behind the framework of the code examples wasn't always obvious (and seemed occasionally overwrought), I think the core statistical concepts come through clearly enough that they could be reimplemented in whatever fashion made most sense to the reader. Generally fairly concise, and generous with graphical outputs as well, which helped solidify conceptual aspects of distributions and their properties.
Good introductory book with interesting example problems. The example code layers abstractions on top of the previously introduced ones from chapter to chapter. Over time it gets hard to comprehend the examples due to class-based polymorphism with multiple levels of inheritance. If not this annoyance, it would be a great book.
Good introduction to Bayesian analysis. I didn't take the time this time through to do all of the code samples and exercises, but I still got a decent overview. One of the best parts was the first really good explanation of the Monty Hall problem that I've seen; I finally understand it!
an easy to follow bayesian guide
I'm not giving this up because I didn't find it interesting. I'm putting it on hold because there are some technical books that I need to read first (for work purposes.)