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An open problem regarding the convergence of universal a priori probability
Is the textbook result that Solomonoff’s universal posterior converges to the true posterior for all Martin-Löf random sequences true
What Can Artificial Intelligence Do for Scientific Realism?
The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for unconceived alternatives providing modal knowledge of what is possible therein. As a result, the epistemic warrant of synthesised realist theories should emerge bolstered as the underdetermination by available evidence gets reduced. While shifting the realist commitment away from theoretical artefacts towards modalities of the possibility spaces, the synthesis comes out as a kind of perspectival modelling
On Universal Prediction and Bayesian Confirmation
The Bayesian framework is a well-studied and successful framework for
inductive reasoning, which includes hypothesis testing and confirmation,
parameter estimation, sequence prediction, classification, and regression. But
standard statistical guidelines for choosing the model class and prior are not
always available or fail, in particular in complex situations. Solomonoff
completed the Bayesian framework by providing a rigorous, unique, formal, and
universal choice for the model class and the prior. We discuss in breadth how
and in which sense universal (non-i.i.d.) sequence prediction solves various
(philosophical) problems of traditional Bayesian sequence prediction. We show
that Solomonoff's model possesses many desirable properties: Strong total and
weak instantaneous bounds, and in contrast to most classical continuous prior
densities has no zero p(oste)rior problem, i.e. can confirm universal
hypotheses, is reparametrization and regrouping invariant, and avoids the
old-evidence and updating problem. It even performs well (actually better) in
non-computable environments.Comment: 24 page
The Kardar-Parisi-Zhang equation and universality class
Brownian motion is a continuum scaling limit for a wide class of random
processes, and there has been great success in developing a theory for its
properties (such as distribution functions or regularity) and expanding the
breadth of its universality class. Over the past twenty five years a new
universality class has emerged to describe a host of important physical and
probabilistic models (including one dimensional interface growth processes,
interacting particle systems and polymers in random environments) which display
characteristic, though unusual, scalings and new statistics. This class is
called the Kardar-Parisi-Zhang (KPZ) universality class and underlying it is,
again, a continuum object -- a non-linear stochastic partial differential
equation -- known as the KPZ equation. The purpose of this survey is to explain
the context for, as well as the content of a number of mathematical
breakthroughs which have culminated in the derivation of the exact formula for
the distribution function of the KPZ equation started with {\it narrow wedge}
initial data. In particular we emphasize three topics: (1) The approximation of
the KPZ equation through the weakly asymmetric simple exclusion process; (2)
The derivation of the exact one-point distribution of the solution to the KPZ
equation with narrow wedge initial data; (3) Connections with directed polymers
in random media. As the purpose of this article is to survey and review, we
make precise statements but provide only heuristic arguments with indications
of the technical complexities necessary to make such arguments mathematically
rigorous.Comment: 57 pages, survey article, 7 figures, addition physics ref. added and
typo's correcte
Universal and Composite Hypothesis Testing via Mismatched Divergence
For the universal hypothesis testing problem, where the goal is to decide
between the known null hypothesis distribution and some other unknown
distribution, Hoeffding proposed a universal test in the nineteen sixties.
Hoeffding's universal test statistic can be written in terms of
Kullback-Leibler (K-L) divergence between the empirical distribution of the
observations and the null hypothesis distribution. In this paper a modification
of Hoeffding's test is considered based on a relaxation of the K-L divergence
test statistic, referred to as the mismatched divergence. The resulting
mismatched test is shown to be a generalized likelihood-ratio test (GLRT) for
the case where the alternate distribution lies in a parametric family of the
distributions characterized by a finite dimensional parameter, i.e., it is a
solution to the corresponding composite hypothesis testing problem. For certain
choices of the alternate distribution, it is shown that both the Hoeffding test
and the mismatched test have the same asymptotic performance in terms of error
exponents. A consequence of this result is that the GLRT is optimal in
differentiating a particular distribution from others in an exponential family.
It is also shown that the mismatched test has a significant advantage over the
Hoeffding test in terms of finite sample size performance. This advantage is
due to the difference in the asymptotic variances of the two test statistics
under the null hypothesis. In particular, the variance of the K-L divergence
grows linearly with the alphabet size, making the test impractical for
applications involving large alphabet distributions. The variance of the
mismatched divergence on the other hand grows linearly with the dimension of
the parameter space, and can hence be controlled through a prudent choice of
the function class defining the mismatched divergence.Comment: Accepted to IEEE Transactions on Information Theory, July 201
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