Julia is particularly attractive for its ability to run in parallel, not only on clusters, but also on the multiple cores of your laptop processor. Here is a minimalistic example using pmap().

# For parallel computing with julia -p NumberOfAdditionalCPU
@everywhere begin # this part will be available on all CPUs
function computation(runid_is_useless)
result = rand()
return result
end # function computation()
end # @everywhere
### Main part - runs only on main instance of Julia.
Nprocs = nprocs()
print("Number of processors ",Nprocs,".\n")
## Launching computations on Nprocs parallel processors
results = @time pmap(computation,1:Nprocs)
## Post-processing
for i in 1:Nprocs
print(@sprintf("Result of CPU %i is %f\n",i,results[i]))
end # for i

On my laptop, my processor has 4 cores, and I run this code with julia -p 3 code.jl which gives

Number of processors 4.
0.548238 seconds (214.75 k allocations: 9.788 MB, 5.76% gc time)
Result of CPU 1 is 0.725449
Result of CPU 2 is 0.377958
Result of CPU 3 is 0.443606
Result of CPU 4 is 0.016641

The same code with -p 39 can also be run on the 40 CPUs cluster of my department! For more material on parallel computing with Julia, you may take a look at Julia documentation.

This tiny post is about some basics on the Gamma law, one of my favorite laws. Recall that the Gamma law \( {\mathrm{Gamma}(a,\lambda)} \) with shape parameter \( {a\in(0,\infty)} \) and scale parameter \( {\lambda\in(0,\infty)} \) is the probability measure on \( {[0,\infty)} \) with density

Recall that \( {\Gamma(n)=(n-1)!} \) for all \( {n\in\{1,2,\ldots\}} \). The normalization \( {\lambda^a/\Gamma(a)} \) is easily recovered from the definition of \( {\Gamma} \) and the fact that if \( {f} \) is a univariate density then the scaled density \( {\lambda f(\lambda\cdot)} \) is also a density for all \( {\lambda>0} \), hence the name “scale parameter” for \( {\lambda} \). So what we have to keep in mind essentially is the Euler Gamma function, which is fundamental in mathematics far beyond probability theory.

The Gamma law has an additive behavior on its shape parameter under convolution, in the sense that for all \( {\lambda\in(0,\infty)} \) and all \( {a,b\in(0,\infty)} \),

The Gamma law with integer shape parameter, known as the Erlang law, is linked with the exponential law since \( {\mathrm{Gamma}(1,\lambda)=\mathrm{Expo}(\lambda)} \) and for all \( {n\in\{1,2,\ldots\}} \)

In queueing theory and telecommunication modeling, the Erlang law \( {\mathrm{Gamma}(n,\lambda)} \) appears typically as the law of the \( {n} \)-th jump time of a Poisson point process of intensity \( {\lambda} \), via the convolution power of exponentials. This provides a first link with the Poisson law.

The Gamma law is also linked with chi-square laws. Recall that the \( {\chi^2(n)} \) law, \( {n\in\{1,2,\ldots\}} \) is the law of \( {\left\Vert Z\right\Vert_2^2=Z_1^2+\cdots+Z_n^2} \) where \( {Z_1,\ldots,Z_n} \) are independent and identically distributed standard Gaussian random variables \( {\mathcal{N}(0,1)} \). The link starts with the identity

We also recover by this way the density of \( {\chi^2(n)} \) via the one of \( {\Gamma(n/2,1/2)} \).

The Gamma law enters the definition of the Dirichlet law. Recall that the Dirichlet law \( {\mathrm{Dirichlet}(a_1,\ldots,a_n)} \) with size parameter \( {n\in\{1,2,\ldots\}} \) and shape parameters \( {(a_1,\ldots,a_n)\in(0,\infty)^n} \) is the law of the self-normalized random vector

When \( {a_1=\cdots=a_n} \) the random variables \( {Z_1,\ldots,Z_n} \) are independent and identically distributed of exponential law and \( {\mathrm{Dirichlet}(1,\ldots,1)} \) is nothing else but the uniform law on the simplex \( {\{(v_1,\ldots,v_n)\in[0,1]:v_1+\cdots+v_n=1\}} \). Note also that the components of the random vector \( {V} \) follow Beta laws namely \( {V_k\sim\mathrm{Beta}(a_k,a_1+\cdots+a_n-a_k)} \) for all \( {k\in\{1,\ldots,n\}} \). Let us finally mention that the density of \( {\mathrm{Dirichlet}(a_1,\ldots,a_n)} \) is given by \( {(v_1,\ldots,v_n)\mapsto\prod_{k=1}^n{v_k}^{a_k-1}/\mathrm{Beta}(a_1,\ldots,a_n)} \) where

The Gamma law is linked with Poisson and Pascal negative-binomial laws, in particular to the geometric law. Indeed the geometric law is an exponential mixture of Poisson laws and more generally the Pascal negative-binomial law, the convolution power of the geometric distribution, is a Gamma mixture of Poisson laws. More precisely, for all \( {p\in[0,1]} \) and \( {n\in\{1,2,\ldots,\}} \), if \( {X\sim\mathrm{Gamma}(n,\ell)} \) with \( {\ell=p/(1−p)} \), and if \( {Y|X\sim\mathrm{Poisson}(X)} \) then \( {Y\sim\mathrm{NegativeBinomial}(n,p)} \), since for all \( {k\geq n} \), \( {\mathbb{P}(Y=k)} \) writes

The Gamma and Poisson laws are deeply connected. Namely if \( {X\sim\mathrm{Gamma}(n,\lambda)} \) with \( {n\in\{1,2,\ldots\}} \) and \( {\lambda\in(0,\infty)} \) and \( {Y\sim\mathrm{Poisson}(r)} \) with \( {r\in(0,\infty)} \) then

Bayesian statisticians are quite familiar with these Gamma-Poisson duality games.

The law \( {\mathrm{Gamma}(n,\lambda)} \) is log-concave when \( {n\geq1} \), and its density as a Boltzmann–Gibbs measure involves the convex energy \( {x\in(0,\infty)\mapsto\lambda x-(n-1)\log(x)} \) and writes

The orthogonal polynomials with respect to \( {\mathrm{Gamma}(a,\lambda)} \) are Laguerre polynomials.

The Gamma law appears in many other situations, for instance in the law of the moduli of the eigenvalues of the complex Ginibre ensemble of random matrices. The multivariate version of the Gamma law is used in mathematical statistics and is connected to Wishart laws which are just multivariate \( {\chi^2} \)-laws. Namely the Wishart law of dimension parameter \( {p} \), sample size parameter \( {n} \), and mean \( {C} \) in the cone \( {\mathbb{S}_p^+} \) of \( {p\times p} \) positive symmetric matrices has density

This post is the written version of a modest tutorial on Large Deviation Principles (LDP). It was given at ICERM during an inter workshops week of the 2018 Semester Program “Point Configurations in Geometry, Physics and Computer Science” organized notably by Ed Saff, Doug Hardin, and Sylvia Serfaty. I was asked to give such a tutorial by PhD students. The audience was heterogeneous, a mixture of old and young mathematicians from different areas.

Foreword. We speak about large deviation theory or techniques. This set of techniques lies between analysis, probability theory, and mathematical physics. It is strongly connected to classical probabilistic limit theorems, to asymptotic analysis, convex analysis, variational analysis, functional analysis, and to Boltzmann–Gibbs measures. It was developed essentially all along the twentieth century. The indian-american mathematician S. R. S. Varadhan was one of the main investigators and he finally received the Abel Prize in 2007 – and many other prizes – for his work on large deviation techniques. He forged a natural, beautiful, unifying concept covering many dispersed cases, and a set of general techniques to play with it. Personally, if you ask me if I like LDPs I will probably answer

“Show me your rate function first, and I will tell you if I like large deviations!”.

As many other things, an LDP can be beautiful and useful, and can also be ugly and useless. Large deviations techniques are so efficient that they have produced some sort of industry, just like many other techniques in mathematics such as moment methods, etc. Sometimes an LDP provides the best intuitive interpretation of asymptotic phenomena, usually via a physical energy variational principle. This is the case for the convergence to the semicircle distribution of the eigenvalues of high dimensional Gaussian random matrices. The closest basic concept related to LDPs is the Laplace method for the asymptotic behavior of integrals or sums.

There are plenty of classical books about large deviation techniques. Here are two:

Refresh on probabilistic notation. A random variable \( {X} \) is a measurable function defined on a set \( {\Omega} \) equipped with a \( {\sigma} \)-field \( {\mathcal{A}} \) and a probability measure \( {\mathbb{P}} \), and taking values in a state space \( {E} \) equipped with a \( {\sigma} \)-field \( {\mathcal{B}} \). When \( {E} \) is a topological space such as \( {\mathbb{R}} \) then the natural choice for \( {\mathcal{B}} \) is the Borel \( {\sigma} \)-field, while if \( {E} \) is discrete we take for \( {\mathcal{B}} \) the set of subsets of \( {E} \). The law \( {\mu} \) of \( {X} \) is the image measure of \( {\mathbb{P}} \) by \( {X} \). Namely, for all \( {A\in\mathcal{A}} \),

The first and last notations are the ones that we use in practice. For any bounded or positive measurable “test” function \( {f:E\rightarrow\mathbb{R}} \), we have the following transportation or transfer formula that allows to compute the expectation of \( {f(X)} \) using the law \( {\mu} \):

Recall also the Markov inequality: if \( {X\geq0} \) is a random variable and \( {x>0} \) a real number, then from \( {x\mathbf{1}_{X\geq x}\leq X} \) we get by taking expectation of both sides that

Motivation and Cramér theorem. Let \( {X_1,X_2,\ldots} \) be a sequence of independent and identically distributed real random variables (we say iid) with law \( {\mu} \) and mean \( {m} \). By the law of large number (LLN) we have, almost surely,

\[ S_n=\frac{X_1+\cdots+X_n}{n} \underset{n\rightarrow\infty}{\longrightarrow} m. \]

It follows that if \( {A\subset\mathbb{R}} \) is a Borel set such that \( {m\not\in\overline{A}} \) (closure of \( {A} \)) then

\[ \mathbb{P}(S_n\in A) \underset{n\rightarrow\infty}{\longrightarrow} 0. \]

We would like to understand at which speed this convergence to zero holds. This amounts to the control of large deviations from the mean, literally. More precisely “large deviation techniques” provide an answer which is asymptotically optimal in \( {n} \) at the exponential scale. Let us reveal the basic idea in the case \( {A=[x,\infty)} \) with \( {x>m} \). We force the exponential and we introduce a free parameter \( {\theta>0} \) that we will optimize later on. By Markov’s inequality

This requires to assume that the random variables have finite exponential moments, meaning that the Laplace transform \( {\varphi} \) of \( {\mu} \) is well defined and finite:

The function \( {\Phi} \) is called the Cramér transform of \( {\mu} \). It is the Legendre transform of the logarithm of the Laplace transform of \( {\mu} \). It can be checked that it is convex and lower semi-continuous with compact level sets, and that \( {m} \) is its unique minimizer, and that \( {\Phi(m)=0} \). With a bit more work we get that for any Borel set \( {A\subset\mathbb{R}} \),

\[ \varlimsup_{n\rightarrow\infty} \frac{1}{n}\log\mathbb{P}(S_n\in A) \leq -\inf_{\overline{A}}\Phi. \]

This upper bound in meaningful when \( {m\not\in\overline A} \) and is trivial otherwise since \( {\Phi(m)=0} \). In fact, this upper bound is tight. Indeed essentially Cramér (\( {\approx} \) 1938) and generally Chernoff (\( {\approx} \) 1952) have shown what we call the Cramér theorem:

\[ \mathbb{P}(S_n\in A) \underset{n\rightarrow\infty}{\approx} \mathrm{e}^{-n\inf_A\Phi} \]

in the sense that in addition to the upper bound the following lower bound holds:

\[ \varliminf_{n\rightarrow\infty} \frac{1}{n}\log\mathbb{P}(S_n\in A) \geq -\inf_{\overset{\circ}{A}}\Phi. \]

The infimum runs over the interior of \( {A} \) this time. Topology plays a role here again. Let us give a rough idea of the proof of the lower bound. We focus on the case where \( {A=(-\delta,\delta)} \) for some \( {\delta>0} \). For all \( {0<\varepsilon<\delta} \), we force the exponential by introducing an arbitrary parameter \( {\eta} \) that we will chose later on, namely

Now if we select \( {\eta} \) in such a way that \( {\widetilde\mu_\eta} \) has zero mean, which turns out to be always possible, then, taking iid random variables \( {\widetilde{X_1},\ldots,\widetilde{X_n}} \) of law \( {\widetilde\mu_\eta} \), the law of large numbers (LLN) gives

This is the germ of the proof of the lower bound. It remains essentially to shift by \( {x} \) to get the lower bound for any ball and then to localize to get it for any open set and then for any Borel set. Note that the upper bound is essentially non-asymptotic and looks like a Chernov deviation inequality while the lower bound is asymptotic and relies here on a weak form of the LLN. Basically, the idea for the upper bound is to use the Markov inequality at the exponential scale, whereas the idea of the lower bound is to tilt the probability measure in order to transform the tail event (or rare event) of interest into a non-tail event. The lower bound is more subtle.

Here is the Cramér transform of some usual discrete and continuous distributions:

Bernoulli \( {p\delta_0+(1-p)\delta_1} \), \( {0<p<1} \), then \( {\Phi(x)=+\infty} \) if \( {x\not\in[0,1]} \) and otherwise

Poisson of mean \( {\lambda>0} \), the \( {\Phi(x)=+\infty} \) if \( {x\leq 0} \) and otherwise

\[ \Phi(x)=\lambda-x+x\log\frac{x}{\lambda}; \]

Exponential of parameter \( {\lambda>0} \), then \( {\Phi(x)=+\infty} \) if \( {x>0} \) and otherwise

\[ \Phi(x)=\lambda x-1-\log(\lambda x); \]

Gaussian of mean \( {m} \) and variance \( {\sigma^2} \), then for all \( {x\in\mathbb{R}} \),

\[ \Phi(x)=\frac{(x-m)^2}{\sigma^2}. \]

The Cramér theorem remains valid for random vectors in \( {\mathbb{R}^d} \), provided that we use the following natural generalization of the Laplace transform and Legendre (and thus Cramér) transform:

Actually the Cramér theorem remains valid for random variables taking values in possibly infinite dimensional spaces provided that we replace the scalar product by the duality. This is know essentially as the Gärtner-Ellis theorem.

Note. A friend of mine, Arnaud Guyader, brought to my attention that Harald Cramér, who was swedish, published his seminal 1938 paper in French in a conference proceedings with the title Sur un nouveau théorème-limite de la théorie des probabilités. Moreover at the occasion of the 80th anniversary of this paper, Hugo Touchette, who knows very well the subject, translated it in English: arXiv:1802.05988.

Cramér was inspired by Esscher. Here is an excerpt from A conversation with S. R. S. Varadhan by Rajendra Bhatia published in The Mathematical Intelligencer 30 (2008), no. 2, 24-42. Bhatia: “Almost all the reports say that the large-deviation principle starts with Cramér”. Varadhan: “The idea comes from the Scandinavian actuarial scientist Fredrik Esscher. He studied the following problem. An insurance company has several clients and each year they make claims which can be thought of as random variables. The company sets aside certain reserves for meeting the claims. What is the probability that the sum of the claims exceeds the reserve set aside? You can use the central limit theorem and estimate this from the tail of the normal distribution. He found that is not quite accurate. To find a better estimate he introduced what is called tilting the measure (Esscher tilting). The value that you want not to be exceeded is not the mean, it is something far out in the tail. You have to change the measure so that this value becomes the mean and again you can use the central limit theorem. This is the basic idea which was generalized by Cramér. Now the method is called the Cramér transform.”

Varadhan general notion of LDP. The Cramér theorem concerns actually the sequence of probability measures \( {{(\mu_n)}_{n\geq1}} \) where \( {\mu_n} \) is the law of \( {S_n} \). More generally, and following Varadhan, we say that a sequence of probability measures \( {{(\mu_n)}_{n\geq1}} \) on a topological space \( {E} \) equipped with its Borel \( {\sigma} \)-field satisfies a large deviation principle with speed \( {{(a_n)}_{n\geq1}} \) and rate function \( {\Phi} \) when:

\( {{(a_n)}_{n\geq1}} \) is an increasing sequence of positive reals with \( {\lim_{n\rightarrow\infty}a_n=+\infty} \);

\( {\Phi:E\rightarrow\mathbb{R}\cup\{\infty\}} \) is a lower semi-continuous function with compact level sets;

for any Borel subset \( {A\subset E} \), we have \( {\mu_n(A) \underset{n\rightarrow\infty}{\approx} \mathrm{e}^{-a_n\inf_A\Phi}} \) in the sense that

This concept is extraordinarily rich, and the Cramér theorem is an instance of it! It can be shown that the rate function is unique and achieves its infimum on \( {E} \) which is equal to \( {0} \).

Note that this implies by the first Borel-Cantelli lemma that if \( {X_n\sim\mu_n} \) for all \( {n\geq1} \), regardless of the way we construct the joint probability space, then \( {{(X_n)}_{n\geq1}} \) tends almost surely to the set of minimizers \( {\arg\inf} \) of \( {\Phi} \). In many (not all) situations, \( {\Phi} \) turns out to be strictly convex and the minimizer is unique, making the sequence \( {{(X_n)}_{n\geq1}} \) converge almost surely to it. In particular, this observation reveals that the Cramér theorem is stronger than the strong law of large numbers (LLN)!

Contraction principle. This principles states that if a sequence of probability measures \( {{(\mu_n)}_{n\geq1}} \) on a topological space \( {E} \) satisfies to an LDP with speed \( {{(a_n)}_{n\geq1}} \) and rate function \( {\Phi:E\rightarrow\mathbb{R}\cup\{\infty\}} \) then for any continuous \( {f:E\rightarrow F} \), the sequence of probability measures \( {{(\mu_n\circ f^{-1})}_{n\geq1}} \) on \( {F} \) satisfies to an LDP with same speed \( {{(a_n)}_{n\geq1}} \) and rate function (with the convention \( {\inf\varnothing=+\infty} \))

\[ y\in F\mapsto \inf_{f^{-1}(y)}\Phi. \]

This allows to deduce many LDPs from a single LDP.

Laplace-Varadhan and Gibbs principles. The first principle, inspired by the Laplace method and due to Varadhan (\( {\approx} \) 1966) states that if \( {{(\mu_n)}_{n\geq1}} \) is a sequence of probability measures on a topological space \( {E} \) which satisfies to an LDP with speed \( {{(a_n)}_{n\geq1}} \) and rate function \( {\Phi} \) and if \( {f:E\rightarrow\mathbb{R}} \) is say bounded and continuous (this can be weakened) then

The second principle, due to Ellis (\( {\approx} \) 1985) states that under the same assumptions the sequence of probability measures \( {{(\nu_n)}_{\geq1}} \) defined by

satisfies to an LDP with speed \( {{(a_n)}_{n\geq1}} \) and rate function

\[ \Phi-f-\inf_E(\Phi-f). \]

This allows to get LDPs for Gibbs measures when the temperature tends to zero. The measure \( {\nu_n} \) is sometimes called the Esscher transform.

Sanov theorem. My favorite LDP! It was proved essentially by Sanov (\( {\approx} \) 1956) and then by Donsker and Varadhan (\( {\approx} \) 1976). If \( {X_1,X_2,\ldots} \) are independent and identically distributed random variables with common law \( {\mu} \) on a Polish space \( {E} \), then the law of large numbers (LLN) states that almost surely, the empirical measure tends to \( {\mu} \), namely

This is weak convergence with respect to continuous and bounded test functions. In other words, almost surely, for every bounded and continuous \( {f:E\rightarrow\mathbb{R}} \),

Let \( {\mu_n} \) be the law of the random empirical measure \( {L_n} \) seen as a random variable taking its values in set \( {\mathcal{P}(E)} \) of probability measures on \( {E} \). Then the Sanov theorem states that \( {{(\mu_n)}_{n\geq1}} \) satisfies to an LDP with speed \( {n} \) and rate function given by the relative entropy \( {H(\cdot\mid\mu)} \). In other words, for any Borel subset \( {A\subset\mathcal{P}(E)} \),

if \( {\nu} \) is aboslutely continuous with respect to \( {\mu} \) and \( {+\infty} \) otherwise. The topology of weak convergence on \( {\mathcal{P}(E)} \) can be metrized with the Bounded-Lipschitz distance

In particular, by taking \( {A=B(\mu,r)^c} \) for this distance, we can see by the first Borel-Cantelli lemma that almost surely, \( {\lim_{n\rightarrow\infty}L_n=\mu} \) weakly. In other words the Sanov theorem is stronger than the strong law of large numbers (LLN). The theorems of Cramér and Sanov are in fact equivalent in the sense that one can derive one from the other either by taking limits or by discretizing.

Let us give an rough idea of the proof of the upper bound. We recall first the variational formula for the entropy: denoting \( {f=\mathrm{d}\nu/\mathrm{d}\mu} \),

This formula expresses \( {H} \) as an (infinite dimensional) Legendre transform log the (infinite dimensional) Laplace transform of \( {\mu} \). To establish this variational formula, it suffices to show that \( {t\in[0,1]\mapsto\alpha(t)=H_\mu(f+t(g-g))} \) is convex, and to use the representation formula of this convex function as an envelope of its tangents:

The convexity of \( {\alpha} \) turns out to come from the convexity of \( {(u,v)\mapsto\beta(u)v^2} \) where \( {\beta(u)=u\log(u)} \) is the function that enters the definition of \( {H} \).

Now, to understand the upper bound of the Sanov theorem, and by analogy with what we did for the Cramér theorem, for any probability measure \( {\nu} \) which plays the role of \( {x} \) and for any test function \( {g\geq0} \) with \( {\int g\mathrm{d}\mu=1} \) which plays the role of \( {\theta} \), let us take a look at the quantity \( {\mathbb{P}(L_n(g)\geq\nu(g))} \), where \( {\nu(g)=\int g\mathrm{d}\nu} \). The Markov inequality gives

We recognize the variational formula for the entropy \( {H} \)! It remains to work.

Boltzmann view on Sanov theorem. For all \( {\varepsilon>0} \) and \( {\nu\in\mathcal{P}(E)} \), the Sanov theorem gives, when used the the ball \( {B(\nu,\varepsilon)} \) for \( {\mathrm{d}_{\mathrm{BL}}} \), the “volumetric” formula

Going further. LDPs are available in a great variety of situations and models. The Schilder (\( {\approx} \) 1962) LDP concerns the law of sample paths of Brownian motion, the Donsker-Varadhan LDP concerns additive functionals of Markov diffusion processes and the Feynman-Kac formula, the Freidlin-Wentzell LDP concerns noisy dynamical systems, etc. The reader may find interesting applications of LDPs in the following course by S.R.S. Varadhan for instance.

where \( {\beta_n>0} \) is a real parameter and \( {Z_n} \) the normalizing constant. In statistical physics \( {\beta_n} \) is proportional to an inverse temperature, \( {H(x_1,\ldots,x_n)} \) is the energy of the configuration of the particles \( {x_1,\ldots,x_n} \) in \( {\mathbb{R}^d} \) of the system, and \( {Z_n} \) is called the partition function. In many situations \( {H_n(x)} \) depends over \( {x} \) only thru the empirical measure \( {L_n=\frac{1}{n}\sum_{i=1}^n\delta_{x_i}} \), for instance via external field and a pair interaction such as

Now let \( {\mu_n} \) be the law of \( {L_n} \) on \( {\mathcal{P}(\mathbb{R}^d)} \) when the atoms \( {(x_1,\ldots,x_n)} \) of \( {L_n} \) follows the law \( {P_n} \). Under mild assumptions on \( {V} \) and \( {W} \) and if \( {\beta_n\gg n} \) it turns out that the sequence \( {{(\mu_n)}_{n\geq1}} \) satisfies to an LDP with speed \( {{(\beta_n)}_{n\geq1}} \) and rate function given by \( {\mathcal{E}-\inf\mathcal{E}} \) with

The quadratic form \( {\mathcal{E}} \) is strictly convex when \( {W} \) is essentially positive in the sense of Bochner, which is the case for Coulomb or Riesz interactions \( {W} \). In this case the first Borel-Cantelli lemma gives that almost surely, and regardless on the way we choose the common probability space, the empirical measure \( {L_n} \) under \( {P_n} \) converges as \( {n\rightarrow\infty} \) to the minimizer \( {\mu_*} \) of \( {\mathcal{\mathbb{R}^d}} \), the equilibrium measure in potential theory.

If \( {\beta_n=n} \) then LDP still holds but with an extra entropic term in the rate namely

and where \( {S(\mu)} \) is the Boltzmann–Shannon entropy of \( {\mu} \) defined by \( {S(\mu)=+\infty} \) if \( {\mu} \) is not absolutely continuous with respect to the Lebesgue measure and otherwise

Also when we turn off the interaction by taking \( {W\equiv0} \) then we recover the Sanov theorem for iid random variables of law \( {\mu_V} \)! By the way, let me tell you a secret: all probability distributions are indeed Boltzmann–Gibbs measures!

Ofer: I wanted to ask you about publications. When one looks at your publication record, a good deal of it is in CPAM (Communications in Pure and Applied Mathematics, the Courant journal), and another striking fact is that almost none (or maybe none at all) is in “big” journals. This does not seem to be an accident… Did you never want to submit to these journals?

Raghu: As a probabilist I wanted to publish in a place that probabilists would read. So I wanted my work to appear in probability journals – as long as it appeared in reasonable probability journals, I did not care where it appeared.

Ofer: It seems that there has been a big change – it used to be that probability papers rarely appeared in those journals, lately it is more common.

Raghu: I never thought of it. For me, CPAM was natural because it was our journal. Every once in a while I would submit elsewhere for a change – few to CMP (Communications in Mathematical Physics) or to the Annals of Probability and PTRF (Probability Theory and Related Fields). The earliest publications are in Indian journals, such as Sankhya. To me, if you have a good paper and it comes out in a journal that people look at, I am happy – it doesn’t really matter where you publish it.

Ofer: And that brings me to another question. Do you think people still look at journals, in the days of arXiv? Are journals important anymore?

Raghu: Indeed, there are no preprints anymore… Journals are important for deans – for promotions, for the institutions. This is what the dean wants – what is the number of your publications, where have you published, what is the impact of the journal; the department needs to put these things together for a promotion to be approved.

Ofer: That’s a somewhat cynical view of the publication world…

Raghu: That’s the only reason we now have journals.

Ofer: So scientifically, their role is over?

Raghu: I think that what would make more sense scientifically is to put your paper on the arXiv, have reviewers read the paper and give their seal of approval. This could be anonymous – there could be a group that requests reviews for articles they deem important, the reviewers read and make their comments, find mistakes etc, and eventually approve. There is no reason for journals – there is no reason for universities to spend millions of dollars to make publishers rich. Publishers have an important role in publishing books, not journals. They don’t make that much money out of it, however.

Ofer: You do have a long experience as editor, both at CPAM and in the Annals of Probability. How does that align with what you just said about journals?

Raghu: 20 years at CPAM… Well, so long as the system is there, you have to play according to the rules.