# Generating Functions for Dice

So last week I was writing an article for Betting Expert about laws of large numbers, and I was trying to produce some representations of distributions to illustrate the Weak LLN and the Central Limit Theorem. Because tossing a coin feels too simplistic, and also because the natural state space for this random variable, at least verbally, is not a subset of the reals, I decided to go for dice instead. So it’s clear what the distribution of the outcome of a single dice roll is, and with a bit of thought or a 6×6 grid, we can work out the distribution of the average of two dice rolls. But what about 100 rolls? Obviously, we need large samples to illustrate the laws of large numbers! In this post, we discuss how to calculate the distribution of the sample mean of n dice rolls.

First we observe that the total set of outcomes of n dice rolls is $6^n$. The sum of the outcomes must lie between n and 6n inclusive. The distribution of the sum and the distribution of the sample mean are equivalent up to dividing by n. The final observation is that because the total number of outcomes has a nice form, we shouldn’t expect it to make any difference to the method if we calculate the probability of a given sum, or the number of configurations giving rise to that sum.

Indeed, tying in nicely with the first year probability course, we are going to use generating functions, and there is no difference in practice between the probability generating function, and the combinatorial generating function, if the underlying mechanism is a uniform choice. Well, in practice, there is a small difference, namely a factor of 6 here. The motivation for using generating functions is clear: we are considering the distribution of a sum of independent random variables. This is pretty much exactly why we bother to set up the machinery for PGFs.

Anyway, since each of {1,2,…,6} is equally likely, the GF of a single dice roll is

$x+x^2+\ldots+x^6=x\cdot \frac{1-x^6}{1-x}.$

So, if we want the generating function of the sum of n independent dice rolls, we can obtain this by raising the above function to the power n. We obtain

$x^n(1-x^6)^n(1-x)^{-n}.$

Note the factor of $x^n$ at the beginning arises because the minimum value of the sum is n. So to work out the number of configurations giving rise to sum k, we need to evaluate the coefficient of $x^k$. We can deal with $(1-x^6)^n$ fairly straightforwardly, but some thought it required regarding whether it’s possible to do similar job on $(1-x)^{-n}$.

We have to engage briefly with what is meant by a binomial coefficient. Note that

$\binom{x}{k}=\frac{x(x-1)\ldots(x-k+1)}{1\cdot\ldots\cdot k}$

is a valid definition even when x is not a positive integer, as it is simply a degree k polynomial in x. This works if x is a general positive real, and indeed if x is a general negative real. At this stage, we do need to keep k a positive integer, but that’s not a problem for our applications.

So we need to engage with how the binomial theorem works for exponents that are not positive integers. The tricky part with the standard expression as

$(a+b)^n=\binom{n}{0}a^n+\ldots + \binom{n}{n}b^n,$

is that the attraction of this symmetry in a and b prompts us to work in more generality than is entirely necessary to state the result. Note if we instead write

$(1+x)^n=1+\binom{n}{1}x+\binom{n}{2}x^2+\ldots,$

we have unwittingly described this finite sum as an infinite series. It just happens that all the binomial coefficients apart from the first (n+1) are zero. The nice thing about this definition is that it might plausibly generalise to non-integer or negative values of n. And indeed it does. I don’t want to go into the details here, but it’s just a Taylor series really, and the binomial coefficients are set up with factorials in the right places to look like a Taylor series, so it all works out.

It is also worth remarking that it follows straight from the definition of a negative binomial coefficient, that

$\binom{-n}{j}=(-1)^j \binom{n+j-1}{j}.$

In any case, we can rewrite our expression for the generating function of the IID sum as

$x^n\left[\sum_{k=0}^n \binom{n}{k}(-1)^k x^{6k}\right]\left[\sum_{j\ge 0} \binom{-n}{j}(-1)^j x^j\right]$

By accounting for where we can gather exponents from each bracket, we can evaluate the coefficient of $x^m$ as

$\sum_{6k+j=m+n}\binom{n}{k}\binom{n+j-1}{j}(-1)^k.$

Ie, k in the sum takes values in $\{0,1,\ldots, \lfloor \frac{m+n}{6}\rfloor\}$. At least in theory, this now gives us an explicit way to calculate the distribution of the average of multiple dice rolls. We have to be wary, however, that many compilers will not be happy dealing with large binomial coefficients, as the large factorials grow extremely rapidly. An approximation using logs is likely to be more tractable for larger settings.

Anyway, I leave you with the fruits of my labours.

Related articles

# Generating Functions for the IMO

The background to this post is that these days I find myself using generating functions all the time, especially for describing the stationary states of various coalescence-like processes. I remember meeting them vaguely while preparing for the IMO as a student. However, a full working understanding must have eluded me at the time, as for Q5 on IMO 2008 in Madrid I had written down in big boxes the two statements involving generating functions that immediately implied the answer, but failed to finish it off. The aim of this post is to help this year’s team avoid that particular pitfall.

What are they?

I’m going to define some things in a way which will be most relevant to the type of problems you are meeting now. Start with a sequence $(a_0,a_1,a_2,\ldots)$. Typically these will be the sizes of various combinatorial sets. Eg a_n = number of partitions of [n] with some property. Define the generating function of the sequence to be:

$f(x)=\sum_{k\geq 0}a_k x^k=a_0+a_1x+a_2x^2+\ldots.$

If the sequence is finite, then this generating function is a polynomial. In general it is a power series. As you may know, some power series can be rather complicated, in terms of where they are defined. Eg

$1+x+x^2+x^3+\ldots=\frac{1}{1-x},$

only when |x|<1. For other values of x, the LHS diverges. Defining f over C is fine too. This sort of thing is generally NOT important for applications of generating functions to combinatorics. To borrow a phrase from Wilf, a generating function is a convenient clothesline’ on which to hang a sequence of numbers.

We need a notation to get back from the generating function to the coefficients. Write $[x^k]g(x)$ to denote the coefficient of $x^k$ in the power series g(x). So, if $g(x)=3x^3-5x^2+7$, then $[x^2]g(x)=-5$. It hopefully should never be relevant unless you read some other notes on the topic, but the notation $[\alpha x^2]g(x):=\frac{[x^2]g(x)}{\alpha}$, which does make sense after a while.

How might they be useful?

Example: binomial coefficients $a_k=\binom{n}{k}$ appear, as the name suggests, as coefficients of

$f_n(x)=(1+x)^n=\sum_{k=0}^n \binom{n}{k}x^k.$

Immediate consequence: it’s trivial to work out $\sum_{k=0}^n \binom{n}{k}$ and $\sum_{k=0}^n(-1)^k \binom{n}{k}$ by substituting $x=\pm 1$ into f_n.

Less obvious consequence. By considering choosing n from a red balls and b blue balls, one can verify

$\binom{a+b}{n}=\sum_{k=0}^n \binom{a}{k}\binom{b}{n-k}.$

We can rewrite the RHS as

$\sum_{k+l=n}\binom{a}{k}\binom{b}{l}.$

Think how we calculate the coefficient of $x^n$ in the product $f(x)g(x)$, and it is now clear that $\binom{a+b}{n}=[x^n](1+x)^{a+b}$, while

$\sum_{k+l=n}\binom{a}{k}\binom{b}{l}=[x^n](1+x)^a(1+x)^b,$

so the result again follows. This provides a good slogan for generating functions: they often replicate arguments via bijections, even if you can’t find the bijection.

Useful for? – Multinomial sums

The reason why the previous argument for binomial coefficients worked nicely is because we were interested in the coefficients, but had a neat expression for the generating function as a polynomial. In particular, we had an expression

$\sum_{k+l=n}a_k b_l.$

This is always a clue that generating functions might be useful. This is sometimes called a convolution.

Exercise: prove that in general, if f(x) is the generating function of (a_k) and g(x) the generating function of (b_l), then f(x)g(x) is the generating function of $\sum_{k+l=n}a_kb_l$.

Even more usefully, this works in the multinomial case:

$\sum_{k_1+\ldots+k_m=n}a^{(1)}_{k_1}\ldots a^{(m)}_{k_m}.$

In many applications, these $a^{(i)}$s will all be the same. We don’t even have to specify how many k_i’s there are to be considered. After all, if we want the sum to be n, then only finitely many can be non-zero. So:

$\sum_{m}\sum_{k_1+\ldots+k_m=n}a_{k_1}\ldots a_{k_m}=[x^n]f(x)^n=[x^n]f(x)^\infty,$

provided f(0)=1.

Useful when? – You recognise the generating function!

In some cases, you can identify the generating function as a standard’ function, eg the geometric series. In that case, manipulating the generating functions is likely to be promising. Here is a list of some useful power series you might spot.

$1+x+x^2+\ldots=\frac{1}{1-x},\quad |x|<1$

$1+2x+3x^2+\ldots=\frac{1}{(1-x)^2},\quad |x|<1$

$e^x=1+x+\frac{x^2}{2!}+\frac{x^3}{3!}+\ldots$

$\cos x=1-\frac{x^2}{2!}+\frac{x^4}{4!}\pm\ldots$

Exercise: if you know what differentiation means, show that if f(x) is the gen fn of (a_k), then xf'(x) is the gen fn of ka_k.

Technicalities: some of these identities are defined only for certain values of x. This may be a problem if they are defined at, say, only a single point, but in general this shouldn’t be the case. In addition, you don’t need to worry about differentiability. You can definition differentiation of power series by $x^n\mapsto nx^{n-1}$, and sort out convergence later if necessary.

Useful for? – Recurrent definitions

The Fibonacci numbers are defined by:

$F_0=F_1=1,\quad F_{n+1}=F_n+F_{n-1},\quad n\geq 1.$

Let F(x) be the generating function of the sequence F_n. So, for n=>1,

$[x^n]F(x)=[x^{n-1}]F(x)+[x^{n-2}]F(x)=[x^n](xF(x)+x^2F(x)),$

and F(0)=1, so we can conclude that:

$F(x)=1+(x+x^2)F(x)\quad\Rightarrow\quad F(x)=\frac{1}{1-x-x^2}.$

Exercise: Find a closed form for the generating function of the Catalan numbers, defined recursively by:

$C_n=C_0C_{n-1}+C_1C_{n-2}+\ldots+C_{n-1}C_0.$

Can you now find the coefficients explicitly for this generating function?

Useful for? – Partitions

Partitions can be an absolute nightmare to work with because of the lack of explicit formulae. Often any attempt at a calculation turns into a massive IEP bash. This prompts a search for bijective or bare-hands arguments, but generating functions can be useful too.

For now (*), let’s assume a partition of [n] means a sequence of positive integers $a_1\geq a_2\geq\ldots\geq a_k$ such that $a_1+\ldots+a_k=n$. Let p(n) be the number of partitions of [n].

(* there are other definitions, in terms of a partition of the set [n] into k disjoint but unlabelled sets. Be careful about definitions, but the methods often extend to whatever framework is required. *)

Exercise: Show that the generating function of p(n) is:

$\left(\frac{1}{1-x}\right)\left(\frac{1}{1-x^2}\right)\left(\frac{1}{1-x^3}\right)\ldots$

Note that if we are interested only in partitions of [n], then we don’t need to consider any terms with exponent greater than n, so if we wanted we could take a finite product instead.

Example: the mint group will remember this problem from the first session in Cambridge:

Show that the number of partitions of [n] with distinct parts is equal to the number of partitions of [n] with odd parts.

Rather than the fiddly bijection argument found in the session, we can now treat this as a simple calculation. The generating function for distinct parts is given by:

$(1+x)(1+x^2)(1+x^3)\ldots,$

while the generating function for odd parts is given by:

$\left(\frac{1}{1-x}\right)\left(\frac{1}{1-x^3}\right)\left(\frac{1}{1-x^5}\right)\ldots.$

Writing the former as

$\left(\frac{1-x^2}{1-x}\right)\left(\frac{1-x^4}{1-x^2}\right)\left(\frac{1-x^6}{1-x^3}\right)\ldots$

shows that these are equal and the result follows.

Other things – Multivariate Generating Functions

If you want to track a sequence in two variables, say $a_{m,n}$, then you can encode this with the bivariate generating function

$f(x,y):=\sum_{m,n\geq 0}a_{m,n}x^my^n.$

The coefficients are then extracted by $[x^ay^b]$ and so on. There’s some interesting stuff on counting lattice paths with this method.

Sums over arithmetic progressions via roots of unity

Note that we can extract both $\sum a_n$ and $\sum (-1)^na_n$ by judicious choice of x in f(x). By taking half the sum or half the difference, we can obtain

$a_0+a_2+a_4+\ldots=\frac12(f(1)+f(-1)),\quad a_1+a_3+a_5+\ldots=\frac12(f(1)-f(-1)).$

Can we do this in general? Yes actually. If you want $a_0+a_k+a_{2k}+\ldots$, this is given by:

$a_0+a_k+a_{2k}+\ldots+\frac{1}{k}\left(f(1)+f(w)+\ldots+f(w^{k-1})\right),$

where $w=e^{2\pi i/k}$ is a $k$th root of unity. Exercise: Prove this.

For greater clarity, first try the case k=4, and consider the complex part of the power series evaluated at +i and -1.

# Exponentials kill Polynomials

I gave my second session at the UK IMO training and selection camp at Trinity Cambridge earlier today. This one to a group of the more experienced students on the subject of polynomials. This always feels like a tricky topic to present to olympiad students. I always felt that there were lots of useful connections between roots and coefficients, but it was hard to get a handle on exactly what sort of relationship would be useful for each question. Perhaps the main problem is that any of the natural interesting things to talk about lie annoyingly on the fringes of complex analysis or abstract algebra. Or, at any rate, are best explained in that language, which isn’t particularly suitable at this stage when there’s only an hour and a half to play with.

One problem I was particularly keen for the students to attempt was a proof that exponential functions always grow faster than polynomials. I think this is a good problem to think about because it is so useful in all sorts of areas. In probability for example, polynomial decay and exponential decay are the two regimes generally discussed for the tail behaviour of distributions of random variables, and all sorts of things are qualitatively different in the two cases. It is also often a useful step in a proof when we need very crude bounds on function.

Anyway, how to prove it? Well the first stage is to prove that a polynomial of degree n+1 dominates any polynomial of strictly smaller degree. I am writing ‘dominate’ to mean, ‘is eventually larger than’, under the assumption that the leading coefficients are always positive. (As this seems easier than sticking modulus signs everywhere.)

This isn’t too hard. If we take

$P(x)=a_nx^n+\ldots+a_1x+a_0,$

then for any $x>|a_n|+\ldots+|a_0|$, we must have $x^{n+1}>P(x)$ eventually.

Now we introduce the exponential function. In most applications, it turns out to be most natural to use $e^x$, but for students who haven’t even necessarily done AS-levels I wasn’t happy using a concept whose definition might be rather unfamiliar.

If you are happy with the Taylor series definition

$e^x:=1+x+\frac{x^2}{2!}+\frac{x^3}{3!}+\ldots$

then the result is not too challenging. Given a polynomial P of degree k with positive leading coefficient, by the previous result we have that eventually

$P(x)<1+x+\frac{x^2}{2!}+\ldots+\frac{x^k}{k!}+\frac{x^{k+1}}{(k+1)!}\leq e^x.$

Although the students were able to follow this proof, they were happier thinking about $P(n)<2^n$. Obviously, we could replace x by n log 2 in the previous argument, but I was pleased with the following direct proof. Ironically, this has much more of the flavour of analysis than the above.

First we can show by induction that $n<2^{n/2}$ for n > 4. It makes sense to take the broader induction hypothesis $4, and then show that in this range “adding 1 gives you a smaller answer than multiplying by the square root of two”.

From the initial result about polynomials dominating smaller degree polynomials, it suffices to prove the result for $P(x)=x^k$, for some k, rather than arbitrary polynomials. Now we can proof this by induction on k, the degree of P. We can prove the base case k = 1 via the previous paragraph.

If $n^k<2^n$ eventually, then $(4n)^k<2^{n+2k}$, so by changing variables, $n^k<2^{n/4+2k}$ eventually, which is in turn $< 2^{n/2}$. So, eventually

$n^{k+1}<2^{n/2}\cdot 2^{n/2}=2^{n}.$

# Analytic vs Probabilistic Arguments for a Supercritical BP

This follows on directly from the previous post. I was originally going to talk only about what follows, but I got rather carried away with the branching process account. I was stuck on a particular exercise, and we ended up coming up with two arguments: one analytic and one probabilistic. Since the typical flavour of this blog is to present problems which show the advantage of the probabilistic approach, it seems only fair to remark on this case, where the analytic method was less interesting, but much simpler.

Recall that we have a supercritical random graph $G(n,\frac{\lambda}{n}), \lambda>1$, and we are considering the rescaled exploration process $S_{nt}$, which has asymptotic mean $\mu_t=1-t-e^{-\lambda t}$. We can calculate similarly an expression for the asymptotic variance

$\frac{\text{Var}(S_{nt})}{n}\rightarrow v_t=e^{-\lambda t}(1-e^{-\lambda t}).$

To use this to verify the result about the size of the giant component, we verify that $\mu_{\zeta_\lambda+x/\sqrt{n}}$ is negative, and has small variance, which would confirm that the giant component has size bounded above by $\zeta_\lambda$ almost surely. A similar argument is required for the lower bound. The variance is a separate matter, but it is therefore necessary that $\mu_t$ should be decreasing at $t=\zeta_\lambda$, that is $\mu_t'=\lambda e^{-\lambda \zeta_\lambda}<0$. This is what we try to prove in the remainder of this post. Recall that in the previous post we have checked that it is equal to zero here.

Heuristic Explanation

$\mu_t$ has been rescaled from the original definition of the exploration process in both size and time-scale so some care is needed to see why this should hold in the limit. Remember that all components apart from the giant component are of size O(log n). So immediately after exhausting the giant component, you are likely to be visiting components of size roughly log n. A time interval of dt for $\mu$ corresponds to ndt for S, during which S will visit some components of size log n and some of O(1) and some in between. In particular, some fixed proportion of vertices are isolated, that is, in a component of size 1.

There is then a complicated size-biasing train of thought. A component of size log n is more likely to come up than an isolated vertex, but there are not as many of them. The log n components push the derivative $\mu_t'$ towards zero, because S_t decreases by 1 over a time-interval of length log n, which gives a gradient of zero in the limit. However, the isolated vertices give a gradient of -1, because S_t decreases by 1 over a time interval of 1. Despite the fact that log n intervals are likely to appear earlier, it still remains the case that after exhausting a component (in particular, at time $t=\zeta_\lambda$, after exhausting the giant component), with some bounded below positive probability you will choose an isolated vertex next. The component size only affects that time-scale if it is O(n), which none of the remaining components are, so the derivative $\mu_{\zeta_\lambda}'$ consists of some complicated weighted mean of 0 and -1. In particular, it is negative.

Analytic solution

Obviously, that won’t do in practice. Suppressing lambdas for ease of notation, the key fact is: $e^{-\lambda \zeta}=1-\zeta$. We want to show that $\lambda e^{-\lambda \zeta}<1$. Substituting

$\lambda=-\frac{\log(1-\zeta)}{\zeta},$

means that it is required to show:

$-\frac{1-\zeta}{\zeta}\log(1-\zeta)<1.$

Differentiating the left hand side gives:

$\frac{\log(1-\zeta)+\zeta}{\zeta^2}<0,$

since of course $\log(1-\zeta)=\zeta+\frac{\zeta^2}{2}+\frac{\zeta^3}{3}+\dots$. So it suffice to check the result for small $\zeta$. But, again using a Taylor series:

$-\frac{1-\zeta}{\zeta}\log(1-\zeta)=1-\frac12\zeta+O(\zeta^2)<1,$

for small $\zeta$. This gives the required result.

Probabilistic Interpretation and Solution

First, we observe that $\lambda e^{-\lambda\zeta}=\lambda(1-\zeta)$ is the expected number of vertices in the first generation of a $\text{Po}(\lambda)$ whose progeny become extinct. This motivates considering the canonical decomposition of a supercritical branching process Z into the skeleton process and the dual process. The skeleton $Z^+$ consists of all vertices which have infinitely many successors. It is relatively easy to show that this is a branching process with offspring distribution $\text{Po}(\lambda\zeta)$ conditioned on being positive. The dual process $Z^*$ is a G-W branching process with offspring distribution $\text{Po}(\lambda)$ conditioned on dying. This is the same as a branching process with offspring distribution $\text{Po}(\lambda(1-\zeta)$, by a sprinkling argument, which says that if we begin with a Poisson number of things, then remove each one independently with some fixed probability, the remaining number of things is Poisson also.

We can construct the original branching process by

• With probability $\zeta$, take the skeleton, and affixe independent copies of $Z^*$ at every vertex in the skeleton.
• With probability $1-\zeta$, just take a copy of $Z^*$.

It is immediately clear that $\lambda(1-\zeta)\leq 1$. After all, the dual process is almost surely finite, so the offspring distribution cannot have expectation greater than 1. Checking that this is strong is more fiddly. The best way I have come up with is to examine the tail of the distribution of total population size of the original branching process.

The total population size T of a branching process has an exponential tail if the offspring distribution is subcritical. It isn’t hugely surprising that this behaves like a large deviation for iid RVs, since in the limit such an event requires a lot of the offspring counts to deviate substantially from the mean. The same holds in the supercritical case, with the additional complication that though the finite tail decays exponential, there is positive probability that the total size will be infinite. In the critical case, however, there is a power-law decay. This is not hugely surprising as it marks the threshhold for the appearance of the infinite population, just as in a multiplicative coalescent at time 1, we have a load of very large components just about to form a giant component. The tool for all of these results is Dwass’s Theorem, which says:

$\mathbb{P}(T=n)=\frac{1}{n}\mathbb{P}(X_1+\ldots+X_n=n-1),$

where $X_1$ are iid with the offspring distribution. When $\mathbb{E}X_1\neq 1$, this is a large deviation event, for which Cramer’s theorem applies (assuming, as is the case for the Poisson distribution, that the offspring distribution has finite variance). When, $\mathbb{E}X=1$, the Central Limit Theorem says that with high probability,

$X_1+\ldots+X_n\in [n-n^{3/4},n+n^{3/4}],$

so, skating over the details of whether everything is exactly uniform within this CLT scaling window,

$\mathbb{P}(T=n)\geq \frac{1}{n}\cdot\frac{1}{2n^{3/4}}.$

The true exponent of the power law decay is substantially slower than this, but the above argument works as a back-of-the-envelope bound.

In particular, if the dual process has mean 1, then the population size of the original branching process is given by taking a distribution with exponential tail with some probability and a distribution with power-law tail with some probability. Obviously the power-law will dominate, which contradicts the assumption that the original branching process was supercritical, and so has an exponential tail.

# Remarkable fact about Brownian Motion #4: The Dirichlet Problem

So this property of Brownian Motion is so elegant, in my opinion, that when I was recently asked what my ‘favourite theorem’ was, I suggested this. With this result, we can use this probabilistic structure to specify solutions to an important PDE, with boundary conditions, over a large class of domains.

Given a domain D, Laplace’s equation is: $\Delta u=0$ on D, and $u=f$ on the boundary dD, where f is any continuous function defined there. This PDE arises wherever the notion of potentials is defined, for example electromagnetism, fluids and thermodynamics.

Theorem: Given suitable regularity conditions on D to be discussed later, Laplace’s equation has a unique solution, given by:

$u(x)=\mathbb{E}_x[f(B_{T_D})]$

Notation: First, what does this mean? Define $T_D:=\inf\{t:B_t\not\in D\}$, to be the time at which a Brownian Motion leaves the domain D. This is a stopping time, and so will be suitable for application of the Strong Markov Property. $\mathbb{E}_x$ means that we are taking expectation with respect to a BM started at x. So informally, we are defining u(x) as: start a BM at x; see where it hits the boundary of D; record the value of f at that point. Then set u(x) to be the expected value of this process.

Existence: First, we are going to check that the solution conjectured is a solution. We will need a lemma:

Lemma: A locally-bounded function u satisfies $\Delta u=0$ on a domain D if and only if it has the property that for every closed ball $\bar{B(x,r)}\subset D$ we have:

$u(x)=\frac{1}{\sigma_{x,r}(S(x,r))}\int_{S(x,r)}u(z)d\sigma_{x,r}(z)$

where $\sigma_{x,r}$ is the surface area measure on the boundary S(x,r) of the ball radius r centred on x. Essentially, this says that u(x) is equal to the average value of u on a ball around x.

Proof of Theorem: First, existence. Set u as specified in the statement of the theorem. Given a Brownian Motion started at x, we have stopping times $T_r corresponding to the hitting times of the ball radius r around x and the boundary dD. The domination condition holds by continuity provided B(x,r) is contained within D. So we may apply the Strong Markov Property:

$\mathbb{E}_x[f(B_{T_D})]=\mathbb{E}_x[\mathbb{E}_x[f(B_{T_D})|\mathcal{F}_{T_r}]]=\mathbb{E}_x[\mathbb{E}_{B_{T_r}}[f(B_{T_D})]]$

By definition, the left hand expression is u(x). But also, because the distribution of $B_{T_r}$ is uniform on S(x,r), the right hand side is equal to:

$\frac{1}{\sigma_{x,r}(S(x,r))}\int_{S(x,r)}u(z)d\sigma_{x,r}(z)$

and so by the lemma, this guarantees that the function u is harmonic on the interior of D.

The lemma can also be used to show uniqueness. Continue reading