# Exponential families

April 19, 2016 — June 28, 2023

functional analysis
probability
statistics

### Assumed audience:

Data scientists who must pretend they can remember statistics

Exponential families! The secret magic at the heart of traditional statistics.

Exponential families are probability distributions that just work, in the sense that and the things we would hope we can do with them, we can. Thus these are the distributions we are taught to handle in statistics classes, and which lead us to undue optimism about statistics more generally, all of which falls apart later. Often, though, we can approximate intractable families by exponential ones or cunning combinations thereof, e.g. in variational inference, so this is not a complete waste of time.

## 1 Background

Michael I. Jordan,

## 2 Natural exponential families

a.k.a. NEFs. The simplest case. Suppose that $$\mathbf{x} \in \mathcal {X} \subseteq \mathbb{R} ^{p}.$$ Then, a natural exponential family of order p has density or mass function of the form: ${\displaystyle f_{X}(\mathbf {x} \mid {\boldsymbol {\theta }})=h(\mathbf {x} )\ \exp {\Big (}{\boldsymbol {\theta }}^{\rm {T}}\mathbf {x} -A({\boldsymbol {\theta }})\ {\Big )}\,\!,}$ where in this case the parameter $$\boldsymbol {\theta }\in \mathbb {R}^{p}.$$

Important members of this sub-family: Gamma, Gaussian, negative binomial, Poisson and binomial.

I mention this family first because it is a good intuition pump. More commonly we consider a more general family.

## 3 (Full-blown) exponential families

In the more general case we allow the natural statistics and the parameters to not be in natural form but rather related by some $$\mathbb{R} ^{p}\to\mathbb{R} ^{p}$$ functions $$T$$ and $$\eta.$$ The non-trivial part is the $$T$$ function — we can always redefine the $$\eta(\theta)$$ to tbe the real parameters rather than $$\theta$$ and in fact we frequently do, calling it the canonical parameterisation. $\displaystyle f_{X}\!\left(\,\mathbf {x} \mid {\boldsymbol {\theta }}\,\right)=h(\mathbf {x} )\,\exp \!{\Big (}\,{\boldsymbol {\eta }}({\boldsymbol {\theta }})\cdot \mathbf {T} (\mathbf {x} )-A({\boldsymbol {\theta }})\,{\Big )}.$ I.e. these are nonlinear transformation of NEFs. We call $$\eta$$ the natural parameter, and $$\mathbf {T}$$ the sufficient statistic, and $$A$$ the log-partition function.

## 4 Natural parameters and sufficient statistics

One of the neat things about the exponential families is that the partition function, natural statistics and natural parameters are informative about each other.

The cumulant-generating function is simply $$K(u|\eta)=A(\eta+u)-A(\eta)$$.

For the natural exponential families, $$T$$ and $$\eta$$ are identities, the mean vector and covariance matrix are $\operatorname {E} [X]=\nabla A({\boldsymbol {\theta }}){\text{ and }}\operatorname {Cov} [X]=\nabla \nabla ^{\rm {T}}A({\boldsymbol {\theta }})$ where $$\nabla$$ is the gradient and $$\nabla \nabla ^{\top}$$ is the Hessian matrix.

## 5 Natural exponential families with quadratic variance functions

A special case with even nicer properties .

Morris (1982):

The normal, Poisson, gamma, binomial, and negative binomial distributions are univariate natural exponential families with quadratic variance functions (the variance is at most a quadratic function of the mean). Only one other such family exists. Much theory is unified for these six natural exponential families by appeal to their quadratic variance property, including infinite divisibility, cumulants, orthogonal polynomials, large deviations, and limits in distribution.

## 6 Conjugate priors

TBD. Diaconis and Ylvisaker (1979)

## 7 PCA

PCA is famous for Gaussian data. I gather there is some sense in which it can be generalised to all exponential families as the Exponential Family PCA .

## 8 For random graphs

Exponential random graph models. TBD

## 10 Curved exponential families

A generalisation I occasionally see is that of curved exponential families. I do not know how these work of if they have enough features to benefit me.

## 11 Squared Neural Families

Another generalisation. See squared neural families.

## 12 References

Altun, Smola, and Hofmann. 2004. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. UAI ’04.
Balkema, and de Haan. 1974. The Annals of Probability.
Brown. 1986. Fundamentals of Statistical Exponential Families: With Applications in Statistical Decision Theory. Lecture Notes-Monograph Series, v. 9.
Brown, Cai, and Zhou. 2010. The Annals of Statistics.
Canu, and Smola. 2006. Neurocomputing.
Charpentier, and Flachaire. 2019. arXiv:1912.11736 [Econ, Stat].
Collins, Dasgupta, and Schapire. 2001. In Advances in Neural Information Processing Systems.
Diaconis, and Ylvisaker. 1979. The Annals of Statistics.
Efron. 1978. The Annals of Statistics.
Fink. 1997.
Fisher, and Tippett. 1928. Mathematical Proceedings of the Cambridge Philosophical Society.
Gurevich, and Stuke. 2019.
Jensen, and Møller. 1991. The Annals of Applied Probability.
Jun Li, and Dacheng Tao. 2013. IEEE Transactions on Neural Networks and Learning Systems.
Jung, Schmutzhard, and Hlawatsch. 2012. arXiv:1210.6516 [Math, Stat].
Khan, and Lin. 2017. In Artificial Intelligence and Statistics.
Khan, and Rue. 2023.
Liu, Dobriban, and Singer. 2017.
Makarov. 2006. The Journal of Operational Risk.
McNeil. 1997. ASTIN Bulletin: The Journal of the IAA.
Mohamed, Ghahramani, and Heller. 2008. In Advances in Neural Information Processing Systems.
Morris. 1982. The Annals of Statistics.
———. 1983. The Annals of Statistics.
Morris, and Lock. 2009. The American Statistician.
Mueller. 2018. arXiv:1802.00762 [Math].
Pickands III. 1975. The Annals of Statistics.
Ranganath, Tang, Charlin, et al. 2015. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics.
Seeger, ed. 2005.
Shen, Huang, and Ye. 2004. Technometrics.
Tansey, Padilla, Suggala, et al. 2015. In Journal of Machine Learning Research.
Tojo, and Yoshino. 2019. arXiv:1811.01394 [Cs, Math, Stat].
Vajda. 1951. Scandinavian Actuarial Journal.
Wainwright, and Jordan. 2008. Graphical Models, Exponential Families, and Variational Inference. Foundations and Trends® in Machine Learning.