Waiting has come to an end … finally the pdf edition of the Lecture Notes on Non-Commutative Distributions has arrived. As a bonus for loyal followers I have added, compared to the actual content of the lecture series, two small sections at the end on what our machinery has to say about Connes embedding problem and the q-Gaussian distribution. Though, don’t expect too much there …
I just noticed that I have a stupid mistake in my random matrix lecture notes (and also in the recording of the corresponding lecture). I am replacing the notes with a new version which corrects this.
In Theorem 4.16, I was claiming that weak convergence is equivalent to convergence of moments, in a setting where all moments exist and the limit is determined by its moments. Of course, this is a too optimistic statement. What is true is the direction that convergence of moments implies weak convergence. That’s the important direction. The other direction would be more of a relevance for combinatorial aficionados like me, as it would allow me to claim that the combinatorial and the analytical approach in such a setting are equivalent. However, the other direction is clearly wrong without some additional assumptions; and thus there are nice-looking situations where one cannot prove weak convergence by dealing with moments.
Of course this is not a new insight. In the context of proving the convergence to the semicircle for Wigner matrices with non-zero mean for the entries we know that we cannot do this with moments (see for example, Remark 11 in Chapter 4 of my book with Jamie).
To get a kind of positive spin out of this annoying mistake, I started to think about what kind of convergence we actually want in our theorems in free probability. Usually our convergence is in distribution, i.e., we are looking on moments – which seems to be the natural thing to do in the multivariate case of several non-commuting operators. However, we can also project things down to the classical world of one variable by taking functions in our operators and ask for the convergence of all such functions. And then there might be a difference whether we ask for weak convergence or for convergence in distribution (i.e., convergence of all moments).
This might become kind of relevant in the context of rational functions. Sheng Yin showed in Non-commutative rational functions in strong convergent random variables that convergence in distribution goes over from polynomials to rational functions (in the case where we assume that the rational function in the limit is a bounded operator) if we assume strong convergence on the level of polynomials (i.e., also convergence of the operator norms). Without the assumption of strong convergence it is easy to see that there are examples (see page 12 of the paper of Sheng) where one has convergence in distribution for the polynomials, but not for the rational functions. However, though one does not have convergence of the moments of the rational function, it is still true in this example that one has weak convergence of the (selfadjoint) rational function. So maybe it could still be the case that, even without strong convergence assumptions, convergence in distribution for polynomials (or maybe weak convergence for polynomials) implies weak convergence for rational functions. At least at the moment we do not know a counter example to this.
This might come a bit late, but just to put it somewhere officially: I have now also put online a pdf-version of the lecture notes of my random matrix class from the last winter term 2019/20.
I have now finally put up a tex-version of the lecture notes of my class “Free Probability Theory” from the winter term 2018/19.