Archive for dominating measure

[very] simple rejection Monte Carlo

Posted in Books, pictures, R, University life with tags , , , , , , , , on March 29, 2024 by xi'an

“In recent years, the Rejection Monte Carlo (RMC) algorithm has emerged sporadically in literature under alternative names such as screening sampling or reject-accept sampling algorithms”

First, I was intrigued enough by a new arXival spotted in the Thalys train from Brussels to take a deeper look at it, but soon realised there was nothing of substance in the paper. Which solely recalls the fundamental of (accept-)reject algorithms, invented in the early days of computer simulation by von Neumann (even though the preprint refers to much more recent publications).  Without providing the average acceptance probability as being equal to the inverse of the bounding constant [independently of the dimension of the random variable] and no mention of The Bible either… But with a standard depiction of accepted vs rejected points as uniformly dispersed on the subgraph of the proposal (as in the above taken from our very own Monte Carlo statistical Methods). Funnily enough, the most basic rejection algorithm, that is, the one based on a uniform sampling from a bounding (hyper)box is illustrated for a Normal target, although the latter has infinite support. And the paper seems to conclude on the appeal of using uniform proposals over bounding boxes, even though the increasing inefficiency against the dimension is well-known. A very simple rejection then, indeed!

why is the likelihood not a pdf?

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , on January 4, 2021 by xi'an

The return of an old debate on X validated. Can the likelihood be a pdf?! Even though there exist cases where a [version of the] likelihood function shows such a symmetry between the sufficient statistic and the parameter, as e.g. in the Normal mean model, that they are somewhat exchangeable w.r.t. the same measure, the question is somewhat meaningless for a number of reasons that we can all link to Ronald Fisher:

  1. when defining the likelihood function, Fisher (in his 1912 undergraduate memoir!) warns against integrating it w.r.t. the parameter: “the integration with respect to m is illegitimate and has no definite meaning with respect to inverse probability”. The likelihood is “is a relative probability only, suitable to compare point with point, but incapable of being interpreted as a probability distribution over a region, or of giving any estimate of absolute probability.” And again in 1922: “[the likelihood] is not a differential element, and is incapable of being integrated: it is assigned to a particular point of the range of variation, not to a particular element of it”.
  2. He introduced the term “likelihood” especially to avoid the confusion: “I perceive that the word probability is wrongly used in such a connection: probability is a ratio of frequencies, and about the frequencies of such values we can know nothing whatever (…) I suggest that we may speak without confusion of the likelihood of one value of p being thrice the likelihood of another (…) likelihood is not here used loosely as a synonym of probability, but simply to express the relative frequencies with which such values of the hypothetical quantity p would in fact yield the observed sample”.
  3. Another point he makes repeatedly (both in 1912 and 1922) is the lack of invariance of the probability measure obtained by attaching a dθ to the likelihood function L(θ) and normalising it into a density: while the likelihood “is entirely unchanged by any [one-to-one] transformation”, this definition of a probability distribution is not. Fisher actually distanced himself from a Bayesian “uniform prior” throughout the 1920’s.

which sums up as the urge to never neglect the dominating measure!

factorisation theorem on densities

Posted in Statistics with tags , , , , , , on December 23, 2020 by xi'an

Another occurrence, while building my final math stat exam for my (quarantined!) third year students, of a question on X validated that led me to write down more precisely an argument for the decomposition of densities in exponential families. Albeit the decomposition is somewhat moot (and lost on the initiator of the question since this person later posted an answer ignoring measures), as it all depends on the choice of the dominating measures over X, T(X), and the slices {x; T(x)=t}. The fact that the slice does depend on t requires the measure to accept a potential dependence on t, in which case the conditional density wrt this measure can as well be constant.

O’Bayes 19/3

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , on July 2, 2019 by xi'an

Nancy Reid gave the first talk of the [Canada] day, in an impressive comparison of all approaches in statistics that involve a distribution of sorts on the parameter, connected with the presentation she gave at BFF4 in Harvard two years ago, including safe Bayes options this time. This was related to several (most?) of the talks at the conference, given the level of worry (!) about the choice of a prior distribution. But the main assessment of the methods still seemed to be centred on a frequentist notion of calibration, meaning that epistemic interpretations of probabilities and hence most of Bayesian answers were disqualified from the start.

In connection with Nancy’s focus, Peter Hoff’s talk also concentrated on frequency valid confidence intervals in (linear) hierarchical models. Using prior information or structure to build better and shrinkage-like confidence intervals at a given confidence level. But not in the decision-theoretic way adopted by George Casella, Bill Strawderman and others in the 1980’s. And also making me wonder at the relevance of contemplating a fixed coverage as a natural goal. Above, a side result shown by Peter that I did not know and which may prove useful for Monte Carlo simulation.

Jaeyong Lee worked on a complex model for banded matrices that starts with a regular Wishart prior on the unrestricted space of matrices, computes the posterior and then projects this distribution onto the constrained subspace. (There is a rather consequent literature on this subject, including works by David Dunson in the past decade of which I was unaware.) This is a smart demarginalisation idea but I wonder a wee bit at the notion as the constrained space has measure zero for the larger model. This could explain for the resulting posterior not being a true posterior for the constrained model in the sense that there is no prior over the constrained space that could return such a posterior. Another form of marginalisation paradox. The crux of the paper is however about constructing a functional form of minimaxity. In his discussion of the paper, Guido Consonni provided a representation of the post-processed posterior (P³) that involves the Dickey-Savage ratio, sort of, making me more convinced of the connection.

As a lighter aside, one item of local information I should definitely have broadcasted more loudly and long enough in advance to the conference participants is that the University of Warwick is not located in ye olde town of Warwick, where there is no university, but on the outskirts of the city of Coventry, but not to be confused with the University of Coventry. Located in Coventry.

 

posterior distribution missing the MLE

Posted in Books, Kids, pictures, Statistics with tags , , , , , , , on April 25, 2019 by xi'an

An X validated question as to why the MLE is not necessarily (well) covered by a posterior distribution. Even for a flat prior… Which in restrospect highlights the fact that the MLE (and the MAP) are invasive species in a Bayesian ecosystem. Since they do not account for the dominating measure. And hence do not fare well under reparameterisation. (As a very much to the side comment, I also managed to write an almost identical and simultaneous answer to the first answer to the question.)