## informed proposals for local MCMC in discrete spaces

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , , , on April 17, 2020 by xi'an

Last year Giacomo Zanella published a paper entitled informed proposals for local MCMC in discrete spaces in JASA. Which I had missed somehow and only discovered through another paper, and which we recently discussed at Paris-Dauphine with graduate students, marooned by COVID-19 . Probability targets in discrete spaces are intrinsically hard[er] to simulate in my opinion if only because there is no natural distance, hence no natural neighbourhood. A random walk proposal like the reference kernel in the paper is not directly calibrated. Without demarginalisation there is neither a clear version of calculus for implementing MALA or HMC. What indeed is HMC on a discrete space? If this requires “embedding the binary space in a continuous space”, it does not sound very enticing if the construct is context dependent.

“This would allow for more moves to be accepted and longer moves to be performed, thus improving the algorithm’s efficiency.”

A interesting aspect of the paper is that for near atomic transition kernels K, informally for small σ’s, the proposal switch to Q finds target x normalising constant as new stationary and close to the actual target. Which incidentally reminded me of our vanilla Rao-Blackwellisation with Randal Douc. This however begets the worry that it may prove unwieldy in continuous cases, as except for Gaussian kernels, the  proposal switch to Q may prove intractable and requires further MCMC steps, in a form of infinite regress. Plus a musing that, were the original kernel K to be replaced with the new Q, another informed proposal transform could be applied to Q. Further infinite regress…

“[The optimality of the Metropolis-Hastings choice of acceptance probability] does not translate to the context of balancing functions.”

The paper indeed exhibits a setting that is rehabilitating Barker’ (1965) version of the acceptance probability, but I never  was very much convinced there was a significant difference in using one or the other. During our virtual (?) discussion, we also wondered at the adaptive abilities of the approach, e.g., selecting among a finite family of g’s (according to which criterion) or parameterising g towards an optimal choice of its parameter. And at the capacity for Rao-Blackwellisation since the proposal have to consider the entire set of neighbours prior to moving to a likely one.

## high dimension Metropolis-Hastings algorithms

Posted in Books, Kids, Mountains, pictures, R, Statistics with tags , , , , , , on January 26, 2016 by xi'an

When discussing high dimension models with Ingmar Schüster Schuster [blame my fascination for accented characters!] the other day, we came across the following paradox with Metropolis-Hastings algorithms. If attempting to simulate from a multivariate standard normal distribution in a large dimension, when starting from the mode of the target, i.e., its mean γ, leaving the mode γis extremely unlikely, given the huge drop between the value of the density at the mode γ and at likely realisations (corresponding to the blue sequence). Even when relying on the very scale that makes the proposal identical to the target! Resorting to a tiny scale like Σ/p manages to escape the unhealthy neighbourhood of the highly unlikely mode (as shown with the brown sequence).

Here is the corresponding R code:

p=100
T=1e3
mh=mu #mode as starting value
vale=rep(0,T)
for (t in 1:T){
prop=mvrnorm(1,mh,sigma/p)
if (log(runif(1))&lt;logdmvnorm(prop,mu,sigma)-
logdmvnorm(mh,mu,sigma)) mh=prop
vale[t]=logdmvnorm(mh,mu,sigma)}


## a programming bug with weird consequences

Posted in Kids, pictures, R, Statistics, University life with tags , , , , , , on November 25, 2015 by xi'an

One student of mine coded by mistake an independent Metropolis-Hastings algorithm with too small a variance in the proposal when compared with the target variance. Here is the R code of this implementation:

#target is N(0,1)
#proposal is N(0,.01)
T=1e5
prop=x=rnorm(T,sd=.01)
ratop=dnorm(prop,log=TRUE)-dnorm(prop,sd=.01,log=TRUE)
ratav=ratop[1]
logu=ratop-log(runif(T))
for (t in 2:T){
if (logu[t]>ratav){
x[t]=prop[t];ratav=ratop[t]}else{x[t]=x[t-1]}
}


It produces outputs of the following shape
which is quite amazing because of the small variance. The reason for the lengthy freezes of the chain is the occurrence with positive probability of realisations from the proposal with very small proposal density values, as they induce very small Metropolis-Hastings acceptance probabilities and are almost “impossible” to leave. This is due to the lack of control of the target, which is flat over the domain of the proposal for all practical purposes. Obviously, in such a setting, the outcome is unrelated with the N(0,1) target!

It is also unrelated with the normal proposal in that switching to a t distribution with 3 degrees of freedom produces a similar outcome:

It is only when using a Cauchy proposal that the pattern vanishes:

## efficient exploration of multi-modal posterior distributions

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

The title of this recent arXival had potential appeal, however the proposal ends up being rather straightforward and hence  anti-climactic! The paper by Hu, Hendry and Heng proposes to run a mixture of proposals centred at the various modes of  the target for an efficient exploration. This is a correct MCMC algorithm, granted!, but the requirement to know beforehand all the modes to be explored is self-defeating, since the major issue with MCMC is about modes that are  omitted from the exploration and remain undetected throughout the simulation… As provided, this is a standard MCMC algorithm with no adaptive feature and I would rather suggest our population Monte Carlo version, given the available information. Another connection with population Monte Carlo is that I think the performances would improve by Rao-Blackwellising the acceptance rate, i.e. removing the conditioning on the (ancillary) component of the index. For PMC we proved that using the mixture proposal in the ratio led to an ideally minimal variance estimate and I do not see why randomising the acceptance ratio in the current case would bring any improvement.

## understanding the Hastings algorithm

Posted in Books, Statistics with tags , , , , , on August 26, 2014 by xi'an

David Minh and Paul Minh [who wrote a 2001 Applied Probability Models] have recently arXived a paper on “understanding the Hastings algorithm”. They revert to the form of the acceptance probability suggested by Hastings (1970):

$\rho(x,y) = s(x,y) \left(1+\dfrac{\pi(x) q(y|x)}{\pi(y) q(x|y)}\right)^{-1}$

where s(x,y) is a symmetric function keeping the above between 0 and 1, and q is the proposal. This obviously includes the standard Metropolis-Hastings form of the ratio, as well as Barker’s (1965):

$\rho(x,y) = \left(1+\dfrac{\pi(x) q(y|x)}{\pi(y) q(x|y)}\right)^{-1}$

which is known to be less efficient by accepting less often (see, e.g., Antonietta Mira’s PhD thesis). The authors also consider the alternative

$\rho(x,y) = \min(\pi(y)/ q(y|x),1)\,\min(q(x|y)/\pi(x),1)$

which I had not seen earlier. It is a rather intriguing quantity in that it can be interpreted as (a) a simulation of y from the cutoff target corrected by reweighing the previous x into a simulation from q(x|y); (b) a sequence of two acceptance-rejection steps, each concerned with a correspondence between target and proposal for x or y. There is an obvious caveat in this representation when the target is unnormalised since the ratio may then be arbitrarily small… Yet another alternative could be proposed in this framework, namely the delayed acceptance probability of our paper with Marco and Clara, one special case being

$\rho(x,y) = \min(\pi_1(y)q(x|y)/\pi_1(x) q(y|x),1)\,\min(\pi_2(y)/\pi_1(x),1)$

where

$\pi(x)\propto\pi_1(x)\pi_2(x)$

is an arbitrary decomposition of the target. An interesting remark in the paper is that any Hastings representation can alternatively be written as

$\rho(x,y) = \min(\pi(y)/k(x,y)q(y|x),1)\,\min(k(x,y)q(x|y)/\pi(x),1)$

where k(x,y) is a (positive) symmetric function. Hence every single Metropolis-Hastings is also a delayed acceptance in the sense that it can be interpreted as a two-stage decision.

The second part of the paper considers an extension of the accept-reject algorithm where a value y proposed from a density q(y) is accepted with probability

$\min(\pi(y)/ Mq(y),1)$

and else the current x is repeated, where M is an arbitrary constant (incl. of course the case where it is a proper constant for the original accept-reject algorithm). Curiouser and curiouser, as Alice would say! While I think I have read some similar proposal in the past, I am a wee intrigued at the appear of using only the proposed quantity y to decide about acceptance, since it does not provide the benefit of avoiding generations that are rejected. In this sense, it appears as the opposite of our vanilla Rao-Blackwellisation. (The paper however considers the symmetric version called the independent Markovian minorizing algorithm that only depends on the current x.) In the extension to proposals that depend on the current value x, the authors establish that this Markovian AR is in fine equivalent to the generic Hastings algorithm, hence providing an interpretation of the “mysterious” s(x,y) through a local maximising “constant” M(x,y). A possibly missing section in the paper is the comparison of the alternatives, albeit the authors mention Peskun’s (1973) result that exhibits the Metropolis-Hastings form as the optimum.