## hard birthday problem

Posted in Books, Kids, R, Statistics with tags , , , , , , , , , on February 4, 2021 by xi'an

From an X validated question, found that WordPress now allows for direct link to pdf documents, like the above paper by my old friend Anirban Das Gupta! The question is about estimating a number M of individuals with N distinct birth dates over a year of T days. After looking around I could not find a simpler representation of the probability for N=r other than (1) in my answer,

$\frac{T!}{(\bar N-r)!}\frac{m!}{T^m} \sum_{(r_1,\ldots,r_m);\\\sum_1^m r_i=r\ \&\\\sum_1^m ir_i=m}1\Big/\prod_{j=1}^m r_j! (j!)^{r_j}$

borrowed from a paper by Fisher et al. (Another Fisher!) Checking Feller leads to the probability (p.102)

${T \choose r}\sum_{\nu=0}^r (-1)^{\nu}{r\choose\nu}\left(1-\frac{T-r+\nu}T \right)^m$

which fits rather nicely simulation frequencies, as shown using

apply(!apply(matrix(sample(1:Nb,T*M,rep=TRUE),T,M),1,duplicated),2,sum)


Further, Feller (1970, pp.103-104) justifies an asymptotic Poisson approximation with parameter$$\lambda(M)=\bar{N}\exp\{-M/\bar N\}$ from which an estimate of$M\$ can be derived. With the birthday problem as illustration (pp.105-106)!

It may be that a completion from N to (R¹,R²,…) where the components are the number of days with one birthdate, two birthdates, &tc. could help design an EM algorithm that would remove the summation in (1) but I did not spend more time on the problem (than finding a SAS approximation to the probability!).

## a neat EM resolution

Posted in Books, Kids, Statistics, University life with tags , , , , , , on February 3, 2021 by xi'an

Read (and answered) this question on X validation about finding the maximum likelihood estimator of a 2×2 Gaussian covariance matrix when some observations are partly missing.  The neat thing is that, in this case, the maximisation step is identical to the maximum likelihood estimation of the 2×2 Gaussian covariance matrix by redefining the empirical covariance matrix into Z and maximising

$-n\log|\Sigma|-\text{trace}(Z\Sigma^{-1})$

in Σ. Nothing involved but fun to explain, nonetheless. (In my final exam this year, no student even approached the EM questions!)

## artificial EM

Posted in Books, Kids, R, Statistics, University life with tags , , , , , , on October 28, 2020 by xi'an

When addressing an X validated question on the use of the EM algorithm when estimating a Normal mean, my first comment was that it was inappropriate since there is no missing data structure to anchor by (right preposition?). However I then reflected upon the infinite number of ways to demarginalise the normal density into a joint density

$$∫ f(x,z;μ)dz = φ(x–μ)$$

from the (slice sampler) call to an indicator function for $$f(x,z;μ)$$ to a joint Normal distribution with an arbitrary correlation. While the joint Normal representation produces a sequence converging to the MLE, the slice representation utterly fails as the indicator functions make any starting value of $$μ$$ a fixed point for EM.

Incidentally, when quoting from Wikipedia on the purpose of the EM algorithm, the following passage

Finding a maximum likelihood solution typically requires taking the derivatives of the likelihood function with respect to all the unknown values, the parameters and the latent variables, and simultaneously solving the resulting equations.

struck me as confusing and possibly wrong since it seems to suggest to seek a maximum in both the parameter and the latent variables. Which does not produce the same value as the observed likelihood maximisation.

## mining gold [ABC in PNAS]

Posted in Books, Statistics with tags , , , , , , , , , , , on March 13, 2020 by xi'an

Johann Brehmer and co-authors have just published a paper in PNAS entitled “Mining gold from implicit models to improve likelihood-free inference”. (Besides the pun about mining gold, the paper also involves techniques named RASCAL and SCANDAL, respectively! For Ratio And SCore Approximate Likelihood ratio and SCore-Augmented Neural Density Approximates Likelihood.) This setup is not ABC per se in that their simulator is used both to generate training data and construct a tractable surrogate model. Exploiting Geyer’s (1994) classification trick of expressing the likelihood ratio as the optimal classification ratio when facing two equal-size samples from one density and the other.

“For all these inference strategies, the augmented data is particularly powerful for enhancing the power of simulation-based inference for small changes in the parameter θ.”

Brehmer et al. argue that “the most important novel contribution that differentiates our work from the existing methods is the observation that additional information can be extracted from the simulator, and the development of loss functions that allow us to use this “augmented” data to more efficiently learn surrogates for the likelihood function.” Rather than starting from a statistical model, they also seem to use a scientific simulator made of multiple layers of latent variables z, where

x=F⁰(u⁰,z¹,θ), z¹=G¹(u¹,z²), z²=G¹(u²,z³), …

although they also call the marginal of x, p(x|θ), an (intractable) likelihood.

“The integral of the log is not the log of the integral!”

The central notion behind the improvement is a form of Rao-Blackwellisation, exploiting the simulated z‘s. Joint score functions and joint likelihood ratios are then available. Ignoring biases, the authors demonstrate that the closest approximation to the joint likelihood ratio and the joint score function that only depends on x is the actual likelihood ratio and the actual score function, respectively. Which sounds like an older EM result, except that the roles of estimate and target quantity are somehow inverted: one is approximating the marginal with the joint, while the marginal is the “best” approximation of the joint. But in the implementation of the method, an estimate of the (observed and intractable) likelihood ratio is indeed produced towards minimising an empirical loss based on two simulated samples. Learning this estimate ê(x) then allows one to use it for the actual data. It however requires fitting a new ê(x) for each pair of parameters. Providing as well an estimator of the likelihood p(x|θ). (Hence the SCANDAL!!!) A second type of approximation of the likelihood starts from the approximate value of the likelihood p(x|θ⁰) at a fixed value θ⁰ and expands it locally as an exponential family shift, with the score t(x|θ⁰) as sufficient statistic.

I find the paper definitely interesting even though it requires the representation of the (true) likelihood as a marginalisation over multiple layers of latent variables z. And does not provide an evaluation of the error involved in the process when the model is misspecified. As a minor supplementary appeal of the paper, the use of an asymmetric Galton quincunx to illustrate an intractable array of latent variables will certainly induce me to exploit it in projects and courses!

[Disclaimer: I was not involved in the PNAS editorial process at any point!]

## alternatives to EM

Posted in Books, Statistics with tags , , , , , , , on January 30, 2019 by xi'an

In an arXived preprint submitted to Computational Statistics & Data Analysis, Chan, Han, and Lim study alternatives to EM for latent class models. That is, mixtures of products of Multinomials. (First occurrence of an indicator function being called the “Iverson bracket function”!) The introduction is fairly extensive given this most studied model. The criticisms of EM laid by the authors are that (a) it does not produce an evaluation of the estimation error, which does not sound correct; (b) the convergence is slow, which is also rather misleading as my [low dimensional] experience with mixtures is that it gets very quickly and apparently linearly  to the vicinity of one of the modes. The argument in favour of alternative non-linear optimisation approaches is that they can achieve quadratic convergence. One solution is a projected Quasi-Newton method, based on a quadratic approximation to the target. With some additional intricacies that make the claim of being “way easier than EM algorithm” somewhat specious. The second approach proposed in the paper is sequential quadratic programming, which incorporates the Lagrange multiplier in the target. While the different simulations in the paper show that EM may indeed call for a much larger number of iterations, the obtained likelihoods all are comparable.