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4 of 5 people found the following review helpful
3.0 out of 5 stars If you already know Bayes' Theorem wait for volume 2, 14 Dec 2013
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This review is from: Proving History: Bayes's Theorem and the Quest for the Historical Jesus (Hardcover)
As a mathematician, and atheist, with an interest in religious belief and the development of religious thought I was instantly attracted by the title of this book. First of all if, like me, you understand Bayes' Theorem, you will probably find reading this book very frustrating. Carrier is targetting a non-mathematical audience and so, often, takes several pages of natural language to describe things that can be expressed in a few lines of equations. On several occasions I found myself having to look back over previous pages to remind myself of the hypothesis or evidence Carrier was discussing - doubly frustrating when reading the Kindle version. Carrier also has a tendency to re-emphasise points several times (sometimes to the extent it verges on a rant) - again if you got the point first time its frustrating. In fact, I found myself speed-reading several pages at a time on several occasions. For me, the book could have been a third the length and not lost anything.

I do have to say that I contacted the author because I thought I'd spotted an error in one of his equations. Within 10 minutes of sending the email, I realised it wasn't an error. But the author emailed me back with a gracious reply, so kudos to him for that.

So if I=Book is interesting and M=mathematically trained, then I would set P(I|M)=0.3. I did find some of the historical discussion (e.g. Matthew's tomb description being inspired by Daniel) interesting. But I was also disappointed that the application of BT to the historicity of Jesus is in a second volume - not an obvious assumption given the title of the book. Unfortunately, I cannot be 'not myself' therefore I cannot provide a value for P(I|~M) no matter how hard I try with Bayes' Theorem (BT). This is the problem that Carrier has. He is trying to argue that BT provides the framework for a Historical Method as a analogue to the Scientific Method. But BT is neither axiomatic nor complete - it is a simple derivation from the law of conditional probability. So Carrier's Historical Method should really be (a) hypothesize; and, (b) evaluate the probability that the hypothesis is true. (b) replaces 'do an experiment' in the Scientific Method. This requires a complete probabilistic framework
rather than just BT.

Carrier spends much of the first chapter explaining why we should only trust professional historians and then only some of them some of the time. A brave gambit given that he is about to step into the fields of mathematical logic and statistics - themselves professional endeavours. I'm sure he's had his fill of mathematicians and statisticians pointing that out to him. I don't actually mind, many breakthroughs do come at the boundaries between disciplines and if it means that historians and editorial boards of historical journals and conferences need to make themselves more numerate then that's a good thing. To all you historians out there, no-one says "I can't do history" so don't come with the "I can't do maths".

Back to the book. It is essentially Bayes Theorem for the innumerate. Unfortunately, you won't get a sense of the importance of Bayes' Theorem in assessing witness testimony or drug trials or any of the other areas where it has made great strides. You also won't get a sense of it being applied to the quest for the historical Jesus - that's volume 2. Something that's not made clear in the Amazon summary. Overall, I applaud the author's advocacy of Bayes' Theorem. But if you have never heard of it before, this is not the book to convince you. By keeping the mathematical content to a bare minimum some of the examples and arguments become rather long and convoluted, so P(I|~M) is unlikely to be 1.
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Showing 1-2 of 2 posts in this discussion
Initial post: 10 Jan 2014 18:50:19 GMT
The probability of enjoying the book is a bit more nuanced than Euclidian Norm's example shows. "I cannot provide a value for P(I|~M)" - i believe someone with an "I can't do maths" attitude would give a low value for P(I|~M) - probably less than 10%.

I am thoroughly enjoying the book - I have good (but not great) maths - Engineering Degree level. So there is a sweet spot for the book: - people who have studied university Stats will find it too basic. People who ran away from maths at the first opportunity will not get past page 1.

I think chapter 6 - "the hard stuff" begins to cover this. The formula itself (as presented here) is quite straight-forward, but getting to agreement for the probability of a subtle hypothesis will prove challenging.

Posted on 13 May 2014 13:45:43 BDT
Telboy says:
Thanks for this review, it's very well-written and informative. It's nice to get the perspective of an expert, much appreciated!
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