I really enjoyed this bit of baseball humor:

The following problem appeared in Volume 97, Issue 3 (2024) of Mathematics Magazine.
Two points
and
are chosen at random (uniformly) from the interior of a unit circle. What is the probability that the circle whose diameter is segment
lies entirely in the interior of the unit circle?
Let be the interior of the circle centered at the origin
with radius
. Also, let
denote the circle with diameter
, and let
be the distance of
from the origin.
In the previous post, we showed that
.
To find , I will integrate over this conditional probability:
,
where is the cumulative distribution function of
. For
,
.
Therefore,
.
To calculate this integral, I’ll use the trigonometric substitution . Then the endpoints
and
become
and
. Also,
. Therefore,
,
confirming the answer I had guessed from simulations.
The following problem appeared in Volume 97, Issue 3 (2024) of Mathematics Magazine.
Two points
and
are chosen at random (uniformly) from the interior of a unit circle. What is the probability that the circle whose diameter is segment
lies entirely in the interior of the unit circle?
As discussed in a previous post, I guessed from simulation that the answer is . Naturally, simulation is not a proof, and so I started thinking about how to prove this.
My first thought was to make the problem simpler by letting only one point be chosen at random instead of two. Suppose that the point is fixed at a distance
from the origin. What is the probability that the point
, chosen at random, uniformly, from the interior of the unit circle, has the desired property?
My second thought is that, by radial symmetry, I could rotate the figure so that the point is located at
. In this way, the probability in question is ultimately going to be a function of
.
There is a very nice way to compute such probabilities since is chosen at uniformly from the unit circle. Let
be the set of all points
within the unit circle that have the desired property. Since the area of the unit circle is
, the probability of desired property happening is
.
Based on the simulations discussed in the previous post, my guess was that was the interior of an ellipse centered at the origin with a semimajor axis of length
and a semiminor axis of length
. Now I had to think about how to prove this.
As noted earlier in this series, the circle with diameter will lie within the unit circle exactly when
, where
is the midpoint of
. So suppose that
has coordinates
, where
is known, and let the coordinates of
be
. Then the coordinates of
will be
,
so that
and
.
Therefore, the condition (again, equivalent to the condition that the circle with diameter
lies within the unit circle) becomes
,
which simplifies to
.
When I saw this, light finally dawned. Given two points and
, called the foci, an ellipse is defined to be the set of all points
so that
, where
is a constant. If the coordinates of
,
, and
are
,
, and
, then this becomes
.
Therefore, the set is the interior of an ellipse centered at the origin with
and
. Furthermore,
is the semimajor axis of the ellipse, while the semiminor axis is equal to
.
At last, I could now return to the original question. Suppose that the point is fixed at a distance
from the origin. What is the probability that the point
, chosen at random, uniformly, from the interior of the unit circle, has the property that the circle with diameter
lies within the unit circle? Since
is a subset of the interior of the unit circle, we see that this probability is equal to
.
In the next post, I’ll use this intermediate step to solve the original question.
The following problem appeared in Volume 97, Issue 3 (2024) of Mathematics Magazine.
Two points
and
are chosen at random (uniformly) from the interior of a unit circle. What is the probability that the circle whose diameter is segment
lies entirely in the interior of the unit circle?
As discussed in the previous post, I guessed from simulation that the answer is . Naturally, simulation is not a proof, and so I started thinking about how to prove this.
My first thought was to make the problem simpler by letting only one point be chosen at random instead of two. Suppose that the point is fixed at a distance
from the origin. What is the probability that the point
, chosen at random, uniformly, from the interior of the unit circle, has the desired property?
My second thought is that, by radial symmetry, I could rotate the figure so that the point is located at
. In this way, the probability in question is ultimately going to be a function of
.
There is a very nice way to compute such probabilities since is chosen at uniformly from the unit circle. Let
be the probability that the point
has the desired property. Since the area of the unit circle is
, the probability of desired property happening is
.
So, if I could figure out the shape of , I could compute this conditional probability given the location of the point
.
But, once again, I initially had no idea of what this shape would look like. So, once again, I turned to simulation with Mathematica. As noted earlier in this series, the circle with diameter will lie within the unit circle exactly when
, where
is the midpoint of
. For my initial simulation, I chose
to have coordinates
.

To my surprise, I immediately recognized that the points had the shape of an ellipse centered at the origin. Indeed, with a little playing around, it looked like this ellipse had a semimajor axis of and a semiminor axis of about
.

My next thought was to attempt to find the relationship between the length of the semiminor axis at the distance of
from the origin. I thought I’d draw of few of these simulations for different values of
and then try to see if there was some natural function connecting
to my guesses. My next attempt was
; as it turned out, it looked like the semiminor axis now had a length of
.

At this point, something clicked: is a Pythagorean triple, meaning that
Also, is very close to
, a very familiar number from trigonometry:
So I had a guess: the semiminor axis has length . A few more simulations with different values of
confirmed this guess. For instance, here’s the picture with
.

Now that I was psychologically certain of the answer for , all that remain was proving that this guess actually worked. That’ll be the subject of the next post.
The following problem appeared in Volume 97, Issue 3 (2024) of Mathematics Magazine.
Two points
and
are chosen at random (uniformly) from the interior of a unit circle. What is the probability that the circle whose diameter is segment
lies entirely in the interior of the unit circle?
As discussed in the previous post, I guessed from simulation that the answer is . Naturally, simulation is not a proof, and so I started thinking about how to prove this.
My first thought was to make the problem simpler by letting only one point be chosen at random instead of two. Suppose that the point is fixed at a distance
from the origin. What is the probability that the point
, chosen at random, uniformly, from the interior of the unit circle, has the desired property?
My second thought is that, by radial symmetry, I could rotate the figure so that the point is located at
. In this way, the probability in question is ultimately going to be a function of
.
There is a very nice way to compute such probabilities since is chosen at uniformly from the unit circle. Let
be the probability that the point
has the desired property. Since the area of the unit circle is
, the probability of desired property happening is
.
So, if I could figure out the shape of , I could compute this conditional probability given the location of the point
.
But, once again, I initially had no idea of what this shape would look like. So, once again, I turned to simulation with Mathematica.
First, a technical detail that I ignored in the previous post. To generate points at random inside the unit circle, one might think to let
and
, where the distance from the origin
is chosen at random between 0 and 1 and the angle
is chosen at random from
. Unfortunately, this simple simulation generates too many points that are close to the origin and not enough that are close to the circle:

To see why this happened, let denote the distance of a randomly chosen point from the origin. Then the event
is the same as saying that the point lies inside the circle centered at the origin with radius
, so that the probability of this event should be
.
However, in the above simulation, was chosen uniformly from
, so that
. All this to say, the above simulation did not produce points uniformly chosen from the unit circle.
To remedy this, we employ the standard technique of using the inverse of the above function , which is clearly
. In other words, we will chose randomly chosen radius to have the form
, where
is chosen uniformly on
. In this way,
,
as required. Making this modification (highlighted in yellow) produces points that are more evenly distributed in the unit circle; any bunching of points or empty spaces are simply due to the luck of the draw.

In the next post, I’ll turn to the simulation of .
The following problem appeared in Volume 97, Issue 3 (2024) of Mathematics Magazine.
Two points
and
are chosen at random (uniformly) from the interior of a unit circle. What is the probability that the circle whose diameter is segment
lies entirely in the interior of the unit circle?
It took me a while to wrap my head around the statement of the problem. In the figure, the points and
are chosen from inside the unit circle (blue). Then the circle (pink) with diameter
has center
, the midpoint of
. Also, the radius of the pink circle is
.
The pink circle will lie entirely the blue circle exactly when the green line containing the origin , the point
, and a radius of the pink circle lies within the blue circle. Said another way, the condition is that the distance
plus the radius of the pink circle is less than 1, or
.
As a first step toward wrapping my head around this problem, I programmed a simple simulation in Mathematica to count the number of times that when points
and
were chosen at random from the unit circle.

In the above simulation, out of about 61,000,000 attempts, 66.6644% of the attempts were successful. This leads to the natural guess that the true probability is . Indeed, the 95% confidence confidence interval
contains
, so that the difference of
from
can be plausibly attributed to chance.
I end with a quick programming note. This certainly isn’t the ideal way to perform the simulation. First, for a fast simulation, I should have programmed in C++ or Python instead of Mathematica. Second, the coordinates of and
are chosen from the unit square, so it’s quite possible for
or
or both to lie outside the unit circle. Indeed, the chance that both
and
lie in the unit disk in this simulation is
, meaning that about
of the simulations were simply wasted. So the only sense that this was a quick simulation was that I could type it quickly in Mathematica and then let the computer churn out a result. (I’ll talk about a better way to perform the simulation in the next post.)
The following problem appeared in Volume 96, Issue 3 (2023) of Mathematics Magazine.
Evaluate the following sums in closed form:
and
.
By using the Taylor series expansions of and
and flipping the order of a double sum, I was able to show that
.
I immediately got to thinking: there’s nothing particularly special about and
for this analysis. Is there a way of generalizing this result to all functions with a Taylor series expansion?
Suppose
,
and let’s use the same technique to evaluate
.
To see why this matches our above results, let’s start with and write out the full Taylor series expansion, including zero coefficients:
,
so that
or
After dropping the zero terms and collecting, we obtain
.
A similar calculation would apply to any even function .
We repeat for
,
so that
,
or
or
.
A similar argument applies for any odd function .
The following problem appeared in Volume 96, Issue 3 (2023) of Mathematics Magazine.
Evaluate the following sums in closed form:
and
.
In the previous two posts, I showed that
;
the technique that I used was using the Taylor series expansions of and
to write
and
as double sums and then interchanging the order of summation.
In the post, I share an alternate way of solving for and
. I wish I could take credit for this, but I first learned the idea from my daughter. If we differentiate
, we obtain
.
Something similar happens when differentiating the series for ; however, it’s not quite so simple because of the
term. I begin by separating the
term from the sum, so that a sum from
to
remains:
.
I then differentiate as before:
.
At this point, we reindex the sum. We make the replacement , so that
and
varies from
to
. After the replacement, we then change the dummy index from
back to
.
With a slight alteration to the term, this sum is exactly the definition of
:
.
Summarizing, we have shown that and
. Differentiating
a second time, we obtain
or
.
This last equation is a second-order nonhomogeneous linear differential equation with constant coefficients. A particular solution, using the method of undetermined coefficients, must have the form . Substituting, we see that
We see that and
which then lead to the particular solution
Since and
are solutions of the associated homogeneous equation
, we conclude that
,
where the values of and
depend on the initial conditions on
. As it turns out, it is straightforward to compute
and
, so we will choose
for the initial conditions. We observe that
and
are both clearly equal to 0, so that
as well.
The initial condition clearly imples that
:
To find , we first find
:
.
Since , we conclude that
, and so
.
The following problem appeared in Volume 96, Issue 3 (2023) of Mathematics Magazine.
Evaluate the following sums in closed form:
and
.
In the previous post, we showed that by writing the series as a double sum and then reversing the order of summation. We proceed with very similar logic to evaluate
. Since
is the Taylor series expansion of , we may write
as
As before, we employ one of my favorite techniques from the bag of tricks: reversing the order of summation. Also as before, the inner sum is inner sum is independent of , and so the inner sum is simply equal to the summand times the number of terms. We see that
.
At this point, the solution for diverges from the previous solution for
. I want to cancel the factor of
in the summand; however, the denominator is
,
and doesn’t cancel cleanly with
. Hypothetically, I could cancel as follows:
,
but that introduces an extra in the denominator that I’d rather avoid.
So, instead, I’ll write as
and then distribute and split into two different sums:
.
At this point, I factored out a power of from the first sum. In this way, the two sums are the Taylor series expansions of
and
:
.
This was sufficiently complicated that I was unable to guess this solution by experimenting with Mathematica; nevertheless, Mathematica can give graphical confirmation of the solution since the graphs of the two expressions overlap perfectly.

The following problem appeared in Volume 96, Issue 3 (2023) of Mathematics Magazine.
Evaluate the following sums in closed form:
and
.
We start with and the Taylor series
.
With this, can be written as
.
At this point, my immediate thought was one of my favorite techniques from the bag of tricks: reversing the order of summation. (Two or three chapters of my Ph.D. theses derived from knowing when to apply this technique.) We see that
.
At this point, the inner sum is independent of , and so the inner sum is simply equal to the summand times the number of terms. Since there are
terms for the inner sum (
), we see
.
To simplify, we multiply top and bottom by 2 so that the first term of cancels:
At this point, I factored out a and a power of
to make the sum match the Taylor series for
:
.
I was unsurprised but comforted that this matched the guess I had made by experimenting with Mathematica.