## asymptotically exact inference in likelihood-free models [a reply from the authors]

Posted in R, Statistics with tags , , , , , , , , , , , , , , , , , on December 1, 2016 by xi'an

[Following my post of lastTuesday, Matt Graham commented on the paper with force détails. Here are those comments. A nicer HTML version of the Markdown reply below is also available on Github.]

Thanks for the comments on the paper!

A few additional replies to augment what Amos wrote:

This however sounds somewhat intense in that it involves a quasi-Newton resolution at each step.

The method is definitely computationally expensive. If the constraint function is of the form of a function from an M-dimensional space to an N-dimensional space, with MN, for large N the dominant costs at each timestep are usually the constraint Jacobian (c/u) evaluation (with reverse-mode automatic differentiation this can be evaluated at a cost of O(N) generator / constraint evaluations) and Cholesky decomposition of the Jacobian product (c/u)(c/u) with O(N³) cost (though in many cases e.g. i.i.d. or Markovian simulated data, structure in the generator Jacobian can be exploited to give a significantly reduced cost). Each inner Quasi-Newton update involves a pair of triangular solve operations which have a O(N²) cost, two matrix-vector multiplications with O(MN) cost, and a single constraint / generator function evaluation; the number of Quasi-Newton updates required for convergence in the numerical experiments tended to be much less than N hence the Quasi-Newton iteration tended not to be the main cost.

The high computation cost per update is traded off however with often being able to make much larger proposed moves in high-dimensional state spaces with a high chance of acceptance compared to ABC MCMC approaches. Even in the relatively small Lotka-Volterra example we provide which has an input dimension of 104 (four inputs which map to ‘parameters’, and 100 inputs which map to ‘noise’ variables), the ABC MCMC chains using the coarse ABC kernel radius ϵ=100 with comparably very cheap updates were significantly less efficient in terms of effective sample size / computation time than the proposed constrained HMC approach. This was in large part due to the elliptical slice sampling updates in the ABC MCMC chains generally collapsing down to very small moves even for this relatively coarse ϵ. Performance was even worse using non-adaptive ABC MCMC methods and for smaller ϵ, and for higher input dimensions (e.g. using a longer sequence with correspondingly more random inputs) the comparison becomes even more favourable for the constrained HMC approach. Continue reading

## simulation under zero measure constraints [a reply]

Posted in Books, pictures, Statistics, University life with tags , , , , , on November 21, 2016 by xi'an

Following my post of last Friday on simulating over zero measure sets, as, e.g., producing a sample with a given maximum likelihood estimator, Dennis Prangle pointed out the recent paper on the topic by Graham and Storkey, and a wee bit later, Matt Graham himself wrote an answer to my X Validated question detailing the resolution of the MLE problem for a Student’s t sample. Including the undoubtedly awesome picture of a 3 observation sample distribution over a non-linear manifold in R³. When reading this description I was then reminded of a discussion I had a few months ago with Gabriel Stolz about his free energy approach that managed the same goal through a Langevin process. Including the book Free Energy Computations he wrote in 2010 with Tony Lelièvre and Mathias Rousset. I now have to dig deeper in these papers, but in the meanwhile let me point out that there is a bounty of 200 points running on this X Validated question for another three days. Offered by Glen B., the #1 contributor to X Validated question for all times.

## thermodynamic Monte Carlo

Posted in Books, Statistics, University life with tags , , , , on June 27, 2014 by xi'an

Michael Betancourt, my colleague from Warwick, arXived a month ago a paper about a differential geometry approach to relaxation. (In the Monte Carlo rather than the siesta sense of the term relaxation!) He is considering the best way to link a simple base measure ϖ to a measure of interest π by the sequence

$\pi_\beta(x) = \dfrac{e^{-\beta\Delta V(x)}\varpi(x)}{Z(\beta)}$

where Z(β) is the normalising constant (or partition function in the  thermodynamic translation). Most methods are highly dependent on how the sequence of β’s is chosen. A first nice result (for me) is that the Kullback-Leibler distance and the partition function are strongly related in that

$K(\pi_\beta,\pi_\eta) \approx (\eta-\beta) \dfrac{\text{d}Z}{\text{d}\beta}$

which means that the variation in the normalising constant is driving the variation in the Kullback-Leibler distance. The next section goes into differential geometry and the remains from my Master course in differential geometry alas are much too scattered for me to even remember some notions like that of a bundle… So, like Andrew, I have trouble making sense of the resulting algorithm, which updates the temperature β along with the position and speed. (It sounds like an extra and corresponding energy term is added to the original Hamiltonian function.) Even the Beta-Binomial

$k|p\sim\mathrm{B}(n,p)\,,\ p\sim\mathrm{Be}(a,b)$

example is somewhat too involved for me.  So I tried to write down the algorithm step by step in this special case. Which led to

1. update β into β-εδp’²
2. update p into p-εδp’
3. update p’ into p’+ε{(1-a)/p+(b-1)/(1-p)}
4. compute the average log-likelihood, λ* under the tempered version of the target (at temperature β)
5. update p’ into p’+2εβ{(1-a)/p+(b-1)/(1-p)}-ε[λ-λ*]p’
6. update p’ into p’+ε{(1-a)/p+(b-1)/(1-p)}
7. update β into β-εδp’²
8. update p into p-εδp’

where p’ denotes the momentum auxiliary variable associated with the kinetic energy. And λ is the current log-likelihood. (The parameter ε was equal to 0.005 and I could not find the value of δ.) The only costly step in the above list is the approximation of the log-likelihood average λ*. The above details make the algorithm quite clear but I am still missing the intuition behind…

## controlled thermodynamic integral for Bayesian model comparison [reply]

Posted in Books, pictures, Running, Statistics, University life with tags , , , , , , , , , , , , on April 30, 2014 by xi'an

Chris Oates wrotes the following reply to my Icelandic comments on his paper with Theodore Papamarkou, and Mark Girolami, reply that is detailed enough to deserve a post on its own:

Thank you Christian for your discussion of our work on the Og, and also for your helpful thoughts in the early days of this project! It might be interesting to speculate on some aspects of this procedure:

(i) Quadrature error is present in all estimates of evidence that are based on thermodynamic integration. It remains unknown how to exactly compute the optimal (variance minimising) temperature ladder “on-the-fly”; indeed this may be impossible, since the optimum is defined via a boundary value problem rather than an initial value problem. Other proposals for approximating this optimum are compatible with control variates (e.g. Grosse et al, NIPS 2013, Friel and Wyse, 2014). In empirical experiments we have found that the second order quadrature rule proposed by Friel and Wyse 2014 leads to substantially reduced bias, regardless of the specific choice of ladder.

(ii) Our experiments considered first and second degree polynomials as ZV control variates. In fact, intuition specifically motivates the use of second degree polynomials: Let us presume a linear expansion of the log-likelihood in θ. Then the implied score function is constant, not depending on θ. The quadratic ZV control variates are, in effect, obtained by multiplying the score function by θ. Thus control variates can be chosen to perfectly correlate with the log-likelihood, leading to zero-variance estimators. Of course, there is an empirical question of whether higher-order polynomials are useful when this Taylor approximation is inappropriate, but they would require the estimation of many more coefficients and in practice may be less stable.

(iii) We require that the control variates are stored along the chain and that their sample covariance is computed after the MCMC has terminated. For the specific examples in the paper such additional computation is a negligible fraction of the total computational, so that we did not provide specific timings. When non-diffegeometric MCMC is used to obtain samples, or when the score is unavailable in closed-form and must be estimated, the computational cost of the procedure would necessarily increase.

For the wide class of statistical models with tractable likelihoods, employed in almost all areas of statistical application, the CTI we propose should provide state-of-the-art estimation performance with negligible increase in computational costs.

## controlled thermodynamic integral for Bayesian model comparison

Posted in Books, pictures, Running, Statistics, University life with tags , , , , , , , , , , on April 24, 2014 by xi'an

Chris Oates, Theodore Papamarkou, and Mark Girolami (all from the University of Warwick) just arXived a paper on a new form of thermodynamic integration for computing marginal likelihoods. (I had actually discussed this paper with the authors on a few occasions when visiting Warwick.) The other name of thermodynamic integration is path sampling (Gelman and Meng, 1998). In the current paper, the path goes from the prior to the posterior by a sequence of intermediary distributions using a power of the likelihood. While the path sampling technique is quite efficient a method, the authors propose to improve it through the recourse to control variates, in order to decrease the variance. The control variate is taken from Mira et al. (2013), namely a one-dimensional temperature-dependent transform of the score function. (Strictly speaking, this is an asymptotic control variate in that the mean is only asymptotically zero.) This control variate is then incorporated within the expectation inside the path sampling integral. Its arbitrary elements are then calibrated against the variance of the path sampling integral. Except for the temperature ladder where the authors use a standard geometric rate, as the approach does not account for Monte Carlo and quadrature errors. (The degree of the polynomials used in the control variates is also arbitrarily set.) Interestingly, the paper mixes a lot of recent advances, from the zero variance notion of Mira et al. (2013) to the manifold Metropolis-adjusted Langevin algorithm of Girolami and Calderhead (2011), uses as a base method pMCMC (Jasra et al., 2007). The examples processed in the paper are regression (where the controlled version truly has a zero variance!) and logistic regression (with the benchmarked Pima Indian dataset), with a counter-example of a PDE interestingly proposed in the discussion section. I quite agree with the authors that the method is difficult to envision in complex enough models. I also did not see mentions therein of the extra time involved in using this control variate idea.

## MCqMC 2014 [day #4]

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , on April 11, 2014 by xi'an

I hesitated in changing the above title for “MCqMSmaug” as the plenary talk I attended this morning was given by Wenzel Jakob, who uses Markov chain Monte Carlo methods in image rendering and light simulation. The talk was low-tech’, with plenty of pictures and animations (incl. excerpts from recent blockbusters!), but it stressed how much proper rending relies on powerful MCMC techniques. One point particularly attracted my attention, namely the notion of manifold exploration as it seemed related to my zero measure recent post. (A related video is available on Jakob’s webpage.) You may then wonder where the connection with Smaug could be found: Wenzel Jakob is listed in the credits of both Hobbit movies for his contributions to the visual effects! (Hey, MCMC made Smaug [visual effects the way they are], a cool argument for selling your next MCMC course! I will for sure include a picture of Smaug in my next R class presentation…) The next sessions of the morning opposed Sobol’s memorial to more technical light rendering and I chose Sobol, esp. because I had missed Art Owen’s tutorial on Sunday, as he gave a short presentation on using Sobol’s criteria to identify variables contributing the most to the variability or extreme values of a function, an extreme value kind of ANOVA, most interesting if far from my simulation area… The afternoon sessions saw MCMC talks by Luke Bornn and Scott Schmidler, both having connection with the Wang-Landau algorithm. Actually, Scott’s talk was the one generating the most animated discussion among all those I attended in MCqMC! (To the point of the chairman getting rather rudely making faces…)

## penalising model component complexity

Posted in Books, Mountains, pictures, Statistics, University life with tags , , , , , , , , , , on April 1, 2014 by xi'an

“Prior selection is the fundamental issue in Bayesian statistics. Priors are the Bayesian’s greatest tool, but they are also the greatest point for criticism: the arbitrariness of prior selection procedures and the lack of realistic sensitivity analysis (…) are a serious argument against current Bayesian practice.” (p.23)

A paper that I first read and annotated in the very early hours of the morning in Banff, when temperatures were down in the mid minus 20’s now appeared on arXiv, “Penalising model component complexity: A principled, practical approach to constructing priors” by Thiago Martins, Dan Simpson, Andrea Riebler, Håvard Rue, and Sigrunn Sørbye. It is a highly timely and pertinent paper on the selection of default priors! Which shows that the field of “objective” Bayes is still full of open problems and significant advances and makes a great argument for the future president [that I am] of the O’Bayes section of ISBA to encourage young Bayesian researchers to consider this branch of the field.

“On the other end of the hunt for the holy grail, “objective” priors are data-dependent and are not uniformly accepted among Bayesians on philosophical grounds.” (p.2)

Apart from the above quote, as objective priors are not data-dependent! (this is presumably a typo, used instead of model-dependent), I like very much the introduction (appreciating the reference to the very recent Kamary (2014) that just got rejected by TAS for quoting my blog post way too much… and that we jointly resubmitted to Statistics and Computing). Maybe missing the alternative solution of going hierarchical as far as needed and ending up with default priors [at the top of the ladder]. And not discussing the difficulty in specifying the sensitivity of weakly informative priors.

“Most model components can be naturally regarded as a flexible version of a base model.” (p.3)

The starting point for the modelling is the base model. How easy is it to define this base model? Does it [always?] translate into a null hypothesis formulation? Is there an automated derivation? I assume this somewhat follows from the “block” idea that I do like but how generic is model construction by blocks?

“Occam’s razor is the principle of parsimony, for which simpler model formulations should be preferred until there is enough support for a more complex model.” (p.4)

I also like this idea of putting a prior on the distance from the base! Even more because it is parameterisation invariant (at least at the hyperparameter level). (This vaguely reminded me of a paper we wrote with George a while ago replacing tests with distance evaluations.) And because it gives a definitive meaning to Occam’s razor. However, unless the hyperparameter ξ is one-dimensional this does not define a prior on ξ per se. I equally like Eqn (2) as it shows how the base constraint takes one away from Jeffrey’s prior. Plus, if one takes the Kullback as an intrinsic loss function, this also sounds related to Holmes’s and Walker’s substitute loss pseudopriors, no? Now, eqn (2) does not sound right in the general case. Unless one implicitly takes a uniform prior on the Kullback sphere of radius d? There is a feeling of one-d-ness in the description of the paper (at least till page 6) and I wanted to see how it extends to models with many (≥2) hyperparameters. Until I reached Section 6 where the authors state exactly that! There is also a potential difficulty in that d(ξ) cannot be computed in a general setting. (Assuming that d(ξ) has a non-vanishing Jacobian as on page 19 sounds rather unrealistic.) Still about Section 6, handling reference priors on correlation matrices is a major endeavour, which should produce a steady flow of followers..!

“The current practice of prior specification is, to be honest, not in a good shape. While there has been a strong growth of Bayesian analysis in science, the research field of “practical prior specification” has been left behind.” (*p.23)

There are still quantities to specify and calibrate in the PC priors, which may actually be deemed a good thing by Bayesians (and some modellers). But overall I think this paper and its message constitute a terrific step for Bayesian statistics and I hope the paper can make it to a major journal.