What the …?!

a new method to solve the transformation of calculus

An hilariously ridiculous email I just received (warning: book cover unrelated):

Good day! this is very important to the “Mathematics” and the related fields,
“The Simulator”,“Probability theory”,”Statistics”,”Numerical Analysis”,
“Cryptography”,“Data mining”,“The big data analysis”and“Artificial Intelligence”.
The transformation of random variables in Calculus is very difficult and sometimes
is impossible to be done. The simulator can get the accuracy and precise simulated data
and the database could be the probability distributution if the data size is 100,000,000
or more. The probabilistic model can be designed and getting the probability distribution
and the coefficient using the simulator.

(1)“The Simulator” has four methods,
1) the basic method is the inverse function of the distribution function,
2) the transformation method to get the simulated data,
3) the numerical analysis method to build the simulated database,
4) the simulated database and the estimated line of random variable to get the simulated data.
(2) “Probability Theory” can use the simulator to a tool.
(3) ”Statistics”, the sampling distribution of the point estimator and the test statistic
can be seen as the transformation equation and the critical point and p value is from
the sampling distribution.
(4) ”Numerical Analysis”, the simulator data can overcome the limit of numerical analysis,
the number of random variables could be more 10000.
(5) “Cryptography”, the simulator of the probabilistic model will derive the lock code
which cannot be unlocked.
(6) “Data mining”, the data set can be a specific probability distribution using
“goodness of fit” or “Curve-fitting” or “Curvilinear”.
1) “goodness of fit”, there are 45 distributions for the null hypothesis.
2) “Curve-fitting”, the estimated line of random variable and the estimated line
of the distribution function.
3) “Curvilinear”, the data set is not arithmetic series.
(7) “The big data analysis”, the number of random variables could be more 10000
about the simulator of the probabilistic model.
(8) “Artificial Intelligence”, the model after analysis can be the transformation
equation, the simulator of the probabilistic model can get the simulated data.

The first book name is “The simulator” will be public, the context contains
(1) The simulation methods,
(2)“Probability Theory”,
(3) ”Statistics” and how to write the statistical package even the population is not
Normal distribution or a special statistical model.
(4)“Cryptography”,
(5)“Explored the arithmetic data”,

back to the Bayesian Choice

Surprisingly (or not?!), I received two requests about some exercises from The Bayesian Choice, one from a group of students from McGill having difficulties solving the above, wondering about the properness of the posterior (but missing the integration of x), to whom I sent back this correction. And another one from the Czech Republic about a difficulty with the term “evaluation” by which I meant (pardon my French!) estimation.

ABC intro for Astrophysics

Today I received in the mail a copy of the short book published by edp sciences after the courses we gave last year at the astrophysics summer school, in Autrans. Which contains a quick introduction to ABC extracted from my notes (which I still hope to turn into a book!). As well as a longer coverage of Bayesian foundations and computations by David Stenning and David van Dyk.

Is that a big number? [book review]

A book I received prior to its publication a few days ago from OXford University Press (OUP), as a book editor for CHANCE (usual provisions apply: the contents of this post will be more or less reproduced in my column in CHANCE when it appears). Copy that I found in my mailbox in Warwick last week and read over the (very hot) weekend.

The overall aim of this book by Andrew Elliott is to encourage numeracy (or fight innumeracy) by making sense of absolute quantities by putting them in perspective, teaching about log scales, visualisation, and divide-and-conquer techniques. And providing a massive list of examples and comparisons, sometimes for page after page… The book is associated with a fairly rich website, itself linked with the many blogs of the author and a myriad of other links and items of information (among which I learned of the recent and absurd launch of Elon Musk’s Tesla car in space! A première in garbage dumping…). From what I can gather from these sites, some (most?) of the material in the book seems to have emerged from the various blog entries.

“Length of River Thames (386 km) is 2 x length of the Suez Canal (193.3 km)”

Maybe I was too exhausted by heat and a very busy week in Warwick for our computational statistics week, the football  2018 World Cup having nothing to do with this, but I could not keep reading the chapters of the book in a continuous manner, suffering from massive information overdump! Being given thousands of entries kills [for me] the appeal of outing weight or sense to large and very large and humongous quantities. And the final vignette in each chapter of pairing of numbers like the one above or the one below

“Time since earliest writing (5200 y) is 25 x time since birth of Darwin (208 y)”

only evokes the remote memory of some kid journal I read from time to time as a kid with this type of entries (I cannot remember the name of the journal!). Or maybe it was a journal I would browse while waiting at the hairdresser’s (which brings back memories of endless waits, maybe because I did not like going to the hairdresser…) Some of the background about measurement and other curios carry a sense of Wikipediesque absolute in their minute details.

A last point of disappointment about the book is the poor graphical design or support. While the author insists on the importance of visualisation on grasping the scales of large quantities, and the webpage is full of such entries, there is very little backup with great graphs to be found in “Is that a big number?” Some of the pictures seem taken from an anonymous databank (where are the towers of San Geminiano?!) and there are not enough graphics. For instance, the fantastic graphics of xkcd conveying the xkcd money chart poster. Or about future. Or many many others…

While the style is sometimes light and funny, an overall impression of dryness remains and in comparison I much more preferred Kaiser Fung’s Numbers rule your world and even more both Guesstimation books!