## QuanTA

Posted in Books, pictures, Running, Statistics, University life with tags , , , , , , , on September 17, 2018 by xi'an

My Warwick colleagues Nick Tawn [who also is my most frequent accomplice to running, climbing and currying in Warwick!] and Gareth Robert have just arXived a paper on QuanTA, a new parallel tempering algorithm that Nick designed during his thesis at Warwick, which he defended last semester. Parallel tempering targets in parallel several powered (or power-tempered) versions of the target distribution. With proposed switches between adjacent targets. An improved version transforms the local values before operating the switches. Ideally, the transform should be the composition of the cdf and inverse cdf, but this is impossible. Linearising the transform is feasible, but does not agree with multimodality, which calls for local transforms. Which themselves call for the identification of the different modes. In QuanTA, they are identified by N parallel runs of the standard, or rather N/2 to avoid dependence issues, and K-means estimates. The paper covers the construction of an optimal scaling of temperatures, in that the difference between the temperatures is scaled [with order 1/√d] so that the acceptance rate for swaps is 0.234. Which in turns induces a practical if costly calibration of the temperatures, especially when the size of the jump is depending on the current temperature. However, this cost issue is addressed in the paper, resorting to the acceptance rate as a proxy for effective sample size and the acceptance rate over run time to run the comparison with regular parallel tempering, leading to strong improvements in the mixture examples examined in the paper. The use of machine learning techniques like K-means or more involved solutions is a promising thread in this exciting area of tempering, where intuition about high temperatures can be actually misleading. Because using the wrong scale means missing the area of interest, which is not the mode!

## optimisation via slice sampling

Posted in Statistics with tags , , , , on December 20, 2012 by xi'an

This morning, over breakfast; I read the paper recently arXived by John Birge et Nick Polson. I was intrigued by the combination of optimisation and of slice sampling, but got a wee disappointed by the paper, in that it proposes a simple form of simulated annealing, minimising f(x) by targeting a small collection of energy functions exp{-τf(x)}. Indeed, the slice sampler is used to explore each of those targets, i.e. for a fixed temperature τ. For the four functions considered in the paper, a slice sampler can indeed be implemented, but this feature could be seen as a marginalia, given that a random walk Metropolis-Hastings algorithm could be used as a proposal mechanism in other cases. The other intriguing fact is that annealing is not used in the traditional way of increasing the coefficient τ along iterations (as in our SAME algorithm), for which convergence issues are much more intricate, but instead stays at the level where a whole (Markov) sample is used for each temperature, the outcomes being then compared in terms of localisation (and maybe for starting at the next temperature value). I did not see any discussion about the selection of the successive temperatures, which usually is a delicate issue in realistic settings, nor of the stopping rule(s) used to decide the maximum has been reached.

## threshold schedules for ABC-SMC

Posted in Statistics, University life with tags , , , , , , , , , on October 17, 2012 by xi'an

Daniel Silk, Sarah Filippi and Michael Stumpf just posted on arXiv a new paper on ABC-SMC. They attack therein a typical problem with SMC (and PMC, and even MCMC!) methods, namely the ability to miss (global) modes of the target due to a poor initial exploration. So, if a proposal is built on previous simulations and if  those simulations have missed an important mode, it is quite likely that this proposal will concentrate on other parts of the space and miss the important mode even more. This is also why simulated annealing and other stochastic optimisation algorithms are so fickle: a wrong parameterisation (like the temperature schedule) induces the sequence to converge to a local optimum rather than to the global optimum. Since sequential ABC is a form of simulated annealing, the decreasing tolerance (or threshold) playing the part of the temperature, it is no surprise that it suffers from this generic disease…

The method proposed in the paper aims at avoiding this difficulty by looking at sudden drops in the acceptance rate curve (as a function of the tolerance ε),

$\aleph_t(\epsilon)=\int p_t(x)\mathbb{I}(\Delta(x,x^\star)\le \epsilon)\,\text{d}x,$

suggesting for threshold the value of ε that maximises the second derivative of this curve. Now, before getting to (at?) the issue of turning this principle into a practical algorithm, let me linger at the motivation for it:

“To see this, one can imagine an ε-ball expanding about the true data; at first the ball only encompasses a small number of particles that were drawn from very close to the global maximum, corresponding to the low gradient at the foot of the shape. Once ε is large enough we are able to accept the relatively large number of particles sitting in the local maximum, which causes the increase in gradient.” (p.5)

Thus, the argument for looking at values of ε preceding fast increases in the acceptance rate ℵ is that we are thus avoiding flatter and lower regions of the posterior support corresponding to a local maximum. It clearly encompasses the case studied by the authors of a global highly concentrated global mode, surrounded by flatter lower modes, but it seems to me that this is not necessarily the only possible reason for changes in the shape of the acceptance probability ℵ. First, we are looking at an ABC acceptance rate, not at a genuine likelihood. Second, this acceptance rate function depends on the current (and necessarily rough) approximate to the genuine predictive, pt. Furthermore, when taking into account this rudimentary replacement of the true likelihood function, it is rather difficult to envision how it impacts the correspondence between a proximity in the data and a proximity in the parameter space. (The toy example is both illuminating and misleading, because it considers a setting where the data is a deterministic transform of the parameter.) I thus think the analysis should refer more strongly to the non-parametric literature and in particular to the k-nearest-neighbour approach recently reformulated by Biau et al.: there is no reason to push the tolerance ε all the way down to zero as this limit does not correspond to the optimal value of the tolerance. If one does not use a non-parametric determination of the “right” tolerance, the lingering question is when and why stopping the sequence of ABC simulations.

The acceptance rate function ℵ is approximated using an unscented transform. I understand neither the implementation of, nor the motivations for, this choice. Given that the function ℵ is a one-dimensional transform of the tolerance and that it actually corresponds to the cdf of the distance between the true data and the (current) pseudo-data, why couldn’t we use smooth versions of a cdf estimate based on the simulated distances, given that these distances are already computed..?  I must be missing something.