New developments in statistics breathlessly told

It's a pretty good history of Bayesian statistic, giving a good overview of the reasons why people are excited about it. Perhaps overly enthusiastic, both exaggerating the differences to other types of statistical reasoning and never making it entirely clear what distinguishes Bayesian from frequentist approaches, nor indeed what statistical reasoning is about to begin with.
The narrator is not the worst I have heard, and generally did a reasonable job of making an understandably modulated aural text. But as is often the case with scientific topics, no thought was given to finding a reader who is actually familiar with the vocabulary or the people. Thus, "theorist" consistently became "theororist", John von Neumann became "Newman", and Jerzy Neyman became "Neiman". Among others. It was still eminently listenable, but irritating.

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6 people found this helpful

excellent & enjoyable

it was as lovely as it is. Author has put significant amount of time and effort.

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Interesting book let down by poor production

Would you be willing to try another one of Laural Merlington’s performances?

The general narration was rather flat but the lack of research into common pronunciation was extremely grating. Not just the foreign (and tricky) words like "Aberystwyth" but even common words are tripped over.

Any additional comments?

I would have preferred a little more detail into the actual theorem.

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2 people found this helpful

If at first you don’t succeed. . . .

This was a long listen. It had rather too many examples of how the theory succeeded.

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Interesting topic but disappointing exposition

The book contains a lot of academic and military history regarding Bayesian statistics, which is moderately interesting. But the actual examination of Bayes theorem/formula is very superficial. I am left quite unsatisfied. The basic Bayes equation is certainly true under a very simple interpretation in terms of predicates and percentages. So where does the controversy arise? In guessing the so-called-prior distribution, it seems. But Bayes formulae itself does not care about what is prior and what is posterior --- it is just a relationship between the conditional probabilities A|B and B|A. And it follows logically from the equation P(A&B) = P(A)*P(B|A) = P(B)*P(A|B), which surely must be true. Do frequentists deny that? That seems unlikely. So after listening carefully to the whole of this book, I am disappointed that I could not clearly explain the difference between a frequentist and Bayesian view.

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A worthwhile challenge

If you know about Bayes this will probably add little to you. However, from a blank slate came a deal of confusion as well as some understanding. At the end I realised that I was Bayesian and happy to be one.

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