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Posts Tagged ‘Probability

“In the space between chaos and shape there was another chance”*…

Prince Hamlet spent a lot of time pondering the nature of chance and probability in William Shakespeare’s tragedy. In the famous “To be or not to be” speech, he notes that we helplessly face “the slings and arrows of outrageous fortune” — though a little earlier in the play he declares that “there’s a special providence in the fall of a sparrow,” suggesting that everything happens because God wills it to be so.

We can hardly fault the prince for holding two seemingly contradictory views about the nature of chance; after all, it is a puzzle that has vexed humankind through the ages. Why are we here? Or to give the question a slightly more modern spin, what sequence of events brought us here, and can we imagine a world in which we didn’t arrive on the scene at all?

It is to biologist Sean B. Carroll’s credit that he’s found a way of taking a puzzle that could easily fill volumes (and probably has filled volumes), and presenting it to us in a slim, non-technical, and fun little book, “A Series of Fortunate Events: Chance and the Making of the Planet, Life, and You.”

Carroll (not to be confused with physicist and writer Sean M. Carroll) gets the ball rolling with an introduction to the key concepts in probability and game theory, but quickly moves on to the issue at the heart of the book: the role of chance in evolution. Here we meet a key historical figure, the 20th-century French biochemist Jacques Monod, who won a Nobel Prize for his work on genetics. Monod understood that genetic mutations play a critical role in evolution, and he was struck by the random nature of those mutations…

Carroll quotes Monod: “Pure chance, absolutely free and blind, at the very root of the stupendous edifice of evolution: This central concept of modern biology is no longer one among other possible or even conceivable hypotheses. It is today the sole conceivable hypothesis, the only one that squares with observed and tested fact.”

“There is no scientific concept, in any of the sciences,” Monod concludes, “more destructive of anthropocentrism than this one.”

From there, it’s a short step to the realization that we humans might never have evolved in the first place…

Preview(opens in a new tab)

The profound impact of randomness in determining destiny: “The Power of Chance in Shaping Life and Evolution.”

See also: “Survival of the Luckiest.”

* Jeanette Winterson, The World and Other Places


As we blow on the dice, we might send carefully-calculated birthday greetings to Gabrielle-Émilie Le Tonnelier de Breteuil, Marquise du Châtelet, the French mathematician and physicist who is probably (if unfairly) better known as Voltaire’s mistress; she was born on this date in 1706.  Fascinated by the work of Newton and Leibniz, she dressed as a man to frequent the cafes where the scientific discussions of the time were held.  Her major work was a translation of Newton’s Principia, for which Voltaire wrote the preface; it was published a decade after her death, and was for many years the only translation of the Principia into French.

Judge me for my own merits, or lack of them, but do not look upon me as a mere appendage to this great general or that great scholar, this star that shines at the court of France or that famed author. I am in my own right a whole person, responsible to myself alone for all that I am, all that I say, all that I do. It may be that there are metaphysicians and philosophers whose learning is greater than mine, although I have not met them. Yet, they are but frail humans, too, and have their faults; so, when I add the sum total of my graces, I confess I am inferior to no one.
– Mme du Châtelet, to Frederick the Great of Prussia


“Science is a process”*…



Paul Klee, “The Bounds of the Intellect,” 1927 (detail)


When my grandfather died last fall, it fell to my sisters and me to sort through the books and papers in his home in East Tennessee. My grandfather was a nuclear physicist, my grandmother a mathematician, and among their novels and magazines were reams of scientific publications. In the wood-paneled study, we passed around great sheaves of papers for sorting, filling the air with dust.

My youngest sister put a pile of yellowing papers in front of me, and I started to leaf through the typewritten letters and scholarly articles. Then my eyes fell on the words fundamental breakthroughspectacular, and revolutionary. Letters from some of the biggest names in physics fell out of the folders, in correspondence going back to 1979.

In this stack, I found, was evidence of a mystery. My grandfather had a theory, one that he believed to be among the most important work of his career. And it had never been published…

The remarkable– and illuminating– story of Veronique (Nikki) Greenwood’s quest to determine whether her grandfather was “a genius or a crackpot”: “My Grandfather Thought He Solved a Cosmic Mystery.”

* T.S. Kuhn, The Structure of Scientific Revolution


As we note that the history of science is, effectively, the history of the instruments developed to help us “see” things smaller, larger, smaller, farther, or outside our human sensory range, we might recall that it was on this date in 1664 that natural philosopher, architect and pioneer of the Scientific Revolution Robert Hooke showed an advance copy of his book Micrographia— a chronicle of Hooke’s observations through various lens– to members of the Royal Society.  The volume (which coined the word “cell” in a biological context) went on to become the first scientific best-seller, and inspired broad interest in the new science of microscopy.

source: Cal Tech

Note that the image above is of an edition of Micrographia dated 1665.  Indeed, while (per the above) the text was previewed to the Royal Society in 1664 (to wit the letter, verso), the book wasn’t published until September, 1665.  Note too that Micrographia is in English (while most scientific books of that time were still in Latin)– a fact that no doubt contributed to its best-seller status.


Written by LW

November 4, 2018 at 1:01 am

“Mathematics, rightly viewed, possesses not only truth, but supreme beauty”*…


Maryam Mirzakhani did not enjoy mathematics to begin with. She dreamed of being an author or politician, but as a top student at her all-girls school in Tehran she was still disappointed when her first-year maths exam went poorly. Her teacher believed her – wrongly – to have no particular affinity with the subject.

Soon that would all change. “My first memory of mathematics is probably the time [my brother] told me about the problem of adding numbers from 1 to 100,” she recalled later. This was the story of Carl Gauss, the 18th-century genius whose schoolteacher set him this problem as a timewasting exercise – only for his precocious pupil to calculate the answer in a matter of seconds.

The obvious solution is simple but slow: 1+2+3+4. Gauss’s solution is quicker to execute, and far more cunning. It goes like this: divide the numbers into two groups: from 1 to 50, and from 51 to 100. Then, add them together in pairs, starting with the lowest (1) and the highest (100), and working inwards (2+99, 3+98, and so on). There are 50 pairs; the sum of each pair is 101; the answer is 5050. “That was the first time I enjoyed a beautiful solution,” Mirzakhani told the Clay Mathematics Institute in 2008.

Since then, her appreciation for beautiful solutions has taken her a long way from Farzanegan middle school. At 17 she won her first gold medal at the International Mathematics Olympiad. At 27 she earned a doctorate from Harvard University. The Blumenthal Award and Satter Prize followed, and in 2014 she became the first woman to be awarded the Fields Medal, the highest honour a mathematician can obtain.

Before this particular brand of wonder became perceptible to Mirzakhani, she experienced feelings many of us can relate to: to the indifferent, her subject can seem “cold”, even “pointless”. Yet those who persist will be rewarded with glimpses of conceptual glory, as if gifted upon them by a capricious god: “The beauty of mathematics,” she warned, “only shows itself to more patient followers.”

This concept of “beauty” found in maths has been referred to over centuries by many others; though, like beauty itself, it is notoriously difficult to define…

For an experienced mathematician, the greatest equations are beautiful as well as useful. Can the rest of us see what they see?  “What makes maths beautiful?

[From The New Humanist, via the ever-illuminating 3 Quarks Daily]

Maryam Mirzakhani died last Friday, a victim of breast cancer; she was 40.  As Peter Sarnak (a mathematician at Princeton University and the Institute for Advanced Study) said, her passing is “a big loss and shock to the mathematical community worldwide.”  See also here.

* Bertrand Russell, A History of Western Philosophy


As we accede to awe, we might spare a thought for Andrey (Andrei) Andreyevich Markov; he died on this date in 1922.  A Russian mathematician, he helped to develop the theory of stochastic processes, especially those now called Markov chains: sequences of random variables in which the future variable is determined by the present variable but is independent of the way in which the present state arose from its predecessors.  (For example, the probability of winning at the game of Monopoly can be determined using Markov chains.)  His work on the study of the probability of mutually-dependent events has been developed and widely applied to the biological and social sciences.



Written by LW

July 20, 2017 at 1:01 am

Fun with numbers!…

Gary Foshee, a collector and designer of puzzles from Issaquah near Seattle walked to the lectern to present his talk. It consisted of the following three sentences: “I have two children. One is a boy born on a Tuesday. What is the probability I have two boys?”

The event was the Gathering for Gardner [see here], a convention held every two years in Atlanta, Georgia, uniting mathematicians, magicians and puzzle enthusiasts. The audience was silent as they pondered the question.

“The first thing you think is ‘What has Tuesday got to do with it?'” said Foshee, deadpan. “Well, it has everything to do with it.” And then he stepped down from the stage.

Read the full story of the conclave– held in honor of the remarkable Martin Gardner, who passed away last year, and in the spirit of his legendary “Mathematical Games” column in Scientific American— in New Scientist…  and find the answer to Gary’s puzzle there– or after the smiling professor below.

“I have two children. One is a boy born on a Tuesday. What is the probability I have two boys?”…  readers may hear a Bayesian echo of the Monty Hall Problem on which (R)D has mused before:

The first thing to remember about probability questions is that everyone finds them mind-bending, even mathematicians. The next step is to try to answer a similar but simpler question so that we can isolate what the question is really asking.

So, consider this preliminary question: “I have two children. One of them is a boy. What is the probability I have two boys?”

This is a much easier question: The way Foshee meant it is, of all the families with one boy and exactly one other child, what proportion of those families have two boys?

To answer the question you need to first look at all the equally likely combinations of two children it is possible to have: BG, GB, BB or GG. The question states that one child is a boy. So we can eliminate the GG, leaving us with just three options: BG, GB and BB. One out of these three scenarios is BB, so the probability of the two boys is 1/3.

Now we can repeat this technique for the original question. Let’s list the equally likely possibilities of children, together with the days of the week they are born in. Let’s call a boy born on a Tuesday a BTu. Our possible situations are:

* When the first child is a BTu and the second is a girl born on any day of the week: there are seven different possibilities.
* When the first child is a girl born on any day of the week and the second is a BTu: again, there are seven different possibilities.
* When the first child is a BTu and the second is a boy born on any day of the week: again there are seven different possibilities.
* Finally, there is the situation in which the first child is a boy born on any day of the week and the second child is a BTu – and this is where it gets interesting.

There are seven different possibilities here too, but one of them – when both boys are born on a Tuesday – has already been counted when we considered the first to be a BTu and the second on any day of the week. So, since we are counting equally likely possibilities, we can only find an extra six possibilities here.

Summing up the totals, there are 7 + 7 + 7 + 6 = 27 different equally likely combinations of children with specified gender and birth day, and 13 of these combinations are two boys. So the answer is 13/27, which is very different from 1/3.

It seems remarkable that the probability of having two boys changes from 1/3 to 13/27 when the birth day of one boy is stated – yet it does, and it’s quite a generous difference at that. In fact, if you repeat the question but specify a trait rarer than 1/7 (the chance of being born on a Tuesday), the closer the probability will approach 1/2.

[See UPDATE, below]

As we remember, with Laplace, that “the theory of probabilities is at bottom nothing but common sense reduced to calculus,” we might ask ourselves what the odds are that on this date in 1964 the World’s Largest Cheese would be manufactured for display in the Wisconsin Pavilion at the 1964-65 World’s Fair.  The 14 1/2′ x 6 1/2′ x 5 1/2′, 17-ton cheddar original– the product of 170,000 quarts of milk from 16,000 cows– was cut and eaten in 1965; but a replica was created and put on display near Neillsville, Wisconsin… next to Chatty Belle, the World’s Largest Talking Cow.

The replica on display (source)

UPDATE: reader Jeff Jordan writes with a critique of the reasoning used above to solve Gary Foshee’s puzzle:

For some reason, mathematicians and non-mathematicians alike develop blind
spots about probability problems when they think they already know the
answer, and are trying to convince others of its correctness. While I agree
with most of your analysis, it has one such blind spot. I’m going move
through a progression of variations on another famous conundrum, trying to
isolate these blind spots and eventually get the point you overlooked.

Bertrand’s Box Paradox: Three identical boxes each have two coins inside:
one has two gold coins, one has two silver coins, and one has a silver coin
and a gold coin. You open one and pull out a coin at random, without seeing
the other. It is gold. What is the probability the other coin is the same

A first approach is to say there were three possible boxes you could pick,
but the information you have rules one out. That leaves two that are still
possible. Since you were equally likely to pick either one before picking a
coin, the probability that this box is GG is 1/2. A second approach is that
there were six coins that were equally likely, and three were gold. But two
of them would have come out of the GG box. Since all three were equally
likely, the probability that this box is GG is 2/3.

This appears to be a true paradox because the “same” theoretical approach –
counting equally likely cases – gives different answers. The resolution of
that paradox – and the first blind spot – is that this is an incorrect
theoretical approach to solving the problem. You never want to merely count
cases, you want to sum the probabilities that each case would produce the
observed result. Counting only works when each case that remains possible
has the same chance of producing the observed result. That is true when you
count the coins, but not when you count the boxes. The probability of
producing a gold coin from the GG box is 1, from the SS box is 0, and from
the GS box is 1/2. The correct answer is 1/(1+0+1/2)=2/3. (A second blind
spot is that you don’t “throw out” the impossible cases, you assign them a
probability of zero. That may seem like a trivial distinction, but it helps
to understand what probabilities other than 1 or 0 mean.)

This problem is mathematically equivalent to the original Monty Hall
Problem: You pick Door #1 hoping for the prize, but before opening it the
host opens Door #3 to show that it is empty. Given the chance, what is the
probability you win by switching to door #2? Let D1, D2, and D3 represent
where the prize is. Assuming the host won’t open your door, and knows where
the prize is so he always opens an empty door, then the probability D2 would
produce the observed result is 1, that D3 would is 0, and that D1 is …
well, let’s say it is 1/2. Just like before, the probability D2 now has the
prize is 1/(1+0+1/2)=2/3.

Why did I waffle about the value of P(D1)? There was a physical difference
with the boxes that produced the explicit result P(GS)=1/2. But here the
difference is logical (based on the location of the prize) and implicit. Do
we really know the host would choose randomly? In fact, if the host always
opens Door #3 if he can, then P(D1)=1 and the answer is 1/(1+0+1)=1/2. Or if
he always opens Door #2 if he can, P(D1)=0 and the answer is 1/(1+0+0)=1.
But if we observe that the host opened Door #2 and assume those same biases,
the results reverse.

To answer the question, we must assume a value for P(D1). Assuming anything
other than P(D1)=1/2 implies a bias on the part of the host, and a different
answer if he opens Door #2. So all we can assume is P(D1)=1/2, and the
answer is again 2/3. That is also the answer if we average the results over
many games with the same host (and a consistent bias, whatever it is). The
answer most “experts” give is really that average, and it is a blind spot
that they are not using all the information they have in the individual

We can make the Box Paradox equivalent to this one by making the random
selection implicit. Someone looks in the chosen box, and picks out a gold
coin. The probability is 2/3 that there is another gold coin if that person
picks randomly, 1/2 if that person always prefers a gold coin, and 1 if that
person always prefers a silver one. Without knowing the preference, we can
only assume this person is unbiased and answer 2/3. Over many experiments,
it will also average out to 2/3 regardless of the bias. And this person
doesn’t even have to show the coin. If we assume he is truthful (and we can
only assume that), the answers are the same if he just says “One coin is

Finally, make a few minor changes to the Box Paradox. Change “silver” to
“bronze.” Let the coins be minted in different years, so that the year
embossed on them is never the same for any two. Add a fourth box so that one
box has an older bronze coin with a younger gold coin, and one has a younger
bronze coin with an older gold coin. Now we can call the boxes BB, BG, GB,
and GG based on this ordering. When our someone says “One coin is bronze,”
we can only assume he is unbiased in picking what kind of coin to name, and
the best answer is 1/(1+1/2+1/2+0)=1/2. If there is a bias, it could be
1/(1+1+1+0)=1/3 or 1/(1+0+0+0)=1, but we can’t assume that. Gee, this sounds
oddly familiar, except for the answer. :)

The answer to all of Gary Foshee’s questions is 1/2. His blind spot is that
he doesn’t define events, he counts cases. An event a set of outcomes, not
an outcome itself. The sample space is the set of all possible outcomes. An
event X must be defined by some property such that every outcome in X has
that property, *and* every outcome with the property is in X. The event he
should use as a condition is not “this family includes a boy (born on a
Tuesday)”, it is “The father of this family chooses to tell you one of up to
two facts in the form ‘my family includes a [gender] (born on a [day]).'”
Since most fathers of two will have two different facts of that form to
choose from, Gary Foshee should have assigned a probability to each, not
merely counted the families that fit the description. The answer is then
(1+12P)/(1+26P), where P is the probability he would tell us “one is a boy
born on a Tuesday” when only one of his two children fit that description.
The only value we can assume for P is 1/2, making the answer
(1+6)/(1+13)=1/2. Not P=1 and (1+12)/(1+26)=13/27.

And the blind spot that almost all experts share, is that this means the
answer to most expressions of the simpler Two Child Problem is also 1/2. It
can be different, but only if the problem statement makes two or three
points explicit:

1) Whatever process led to your knowledge of one child’s gender had access
to both children’s genders (and days of birth).
2) That process was predisposed to mention boys over girls (and Tuesdays
over any other day).
3) That process would never mention facts about both children.

When Gary Foshee tells you about one of his kids, #2 is not satisfied. He
probably had a choice of two facts to tell you, and we can’t assume he was
biased towards “boy born on Tuesday.” Just like Monty Hall’s being able to
choose two doors changes the answer from 1/2 to 2/3, Gary Foshee’s being
able to choose two facts changes the answer from 13/27 to 1/2. It is only
13/27 if he was forced to mention that fact, which is why that answer is

Other readers are invited to contribute their thoughts.

By the numbers…

Mark Twain quotes Disraeli: “There are three kinds of lies:  lies, damned lies, and statistics”;  H.G. Wells avers that “Satan delights equally in statistics and in quoting scripture”; but the remarkable Hans Rosling begs to differ…

Rosling, a physician and medical researcher who co-founded Médecins sans Frontièrs (Doctors without Borders) Sweden and the Gapminder Foundation (with his son and daughter-in-law), and developed the Trendalyzer software that represents national and global statistics as animated interactive graphics (e.g., here), ha become a superstar on the lecture circuit.  He brings his unique insight and approach to the BBC with The Joy of Stats

It’s above at full length, so takes a while to watch in toto— but odds are that one will enjoy it!  [UPDATE:  since this post was published, the full version has been rendered “private”; unless and until it’s reposted in full, the taste above will have to do. Readers in the UK (or readers with VPNs that terminate in the UK) can see the full show soon after it airs on BBC Four on Thursday the 13th on the BBC iPlayer.  As a further consolation, here is statistician Andrew Gelman’s “Five Books” interview– his choice of the five best books on statistics– for The Browser. ]

As we realize that sometimes we can, after all, count on it, we might recall that it was on this date in 1776 that Thomas Paine (originally anonymously) published his case for the independence of the American Colonies, “Common Sense”… and after all, as Pierre-Simon, marquis de Laplace pointed out (in 1820), “the theory of probabilities is at bottom nothing but common sense reduced to calculus.”

source: University of Indiana

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