Approximate Discrete Probability Distribution Representation using a Multi-ResolutionBinary Tree

Computing and storing probabilities is a hard problem as soon as one has to deal with complex distributions over multiples random variables. The problem of efficient representation of probability distributions is central in term of computational efficiency in the field of probabilistic reasoning. The main problem arises when dealing with joint probability distributions over a set of random variables: they are always represented using huge probability arrays. In this paper, a new method based on a binary-tree representation is introduced in order to store efficiently very large joint distributions. Our approach approximates any multidimensional joint distributions using an adaptive discretization of the space. We make the assumption that the lower is the probability mass of a particular region of feature space, the larger is the discretization step. This assumption leads to a very optimized representation in term of time and memory. The other advantages of our approach are the ability to refine dynamically the distribution every time it is needed leading to a more accurate representation of the probability distribution and to an anytime representation of the distribution

Why the Quantum Must Yield to Gravity

After providing an extensive overview of the conceptual elements -- such as Einstein's `hole argument' -- that underpin Penrose's proposal for gravitationally induced quantum state reduction, the proposal is constructively criticised. Penrose has suggested a mechanism for objective reduction of quantum states with postulated collapse time T = h/E, where E is an ill-definedness in the gravitational self-energy stemming from the profound conflict between the principles of superposition and general covariance. Here it is argued that, even if Penrose's overall conceptual scheme for the breakdown of quantum mechanics is unreservedly accepted, his formula for the collapse time of superpositions reduces to T --> oo (E --> 0) in the strictly Newtonian regime, which is the domain of his proposed experiment to corroborate the effect. A suggestion is made to rectify this situation. In particular, recognising the cogency of Penrose's reasoning in the domain of full `quantum gravity', it is demonstrated that an appropriate experiment which could in principle corroborate his argued `macroscopic' breakdown of superpositions is not the one involving non-rotating mass distributions as he has suggested, but a Leggett-type SQUID or BEC experiment involving superposed mass distributions in relative rotation. The demonstration thereby brings out one of the distinctive characteristics of Penrose's scheme, rendering it empirically distinguishable from other state reduction theories involving gravity. As an aside, a new geometrical measure of gravity-induced deviation from quantum mechanics in the manner of Penrose is proposed, but now for the canonical commutation relations [Q, P] = ih.Comment: 33 pages (TeX, uses mtexsis) plus 3 figures (epsf). To appear in ``Physics Meets Philosophy at the Planck Scale'' (Cambridge University Press). Two footnotes adde

Conservative reasoning about epistemic uncertainty for the probability of failure on demand of a 1-out-of-2 software-based system in which one channel is “possibly perfect”

In earlier work, (Littlewood and Rushby 2012) (henceforth LR), an analysis was presented of a 1-out-of-2 software-based system in which one channel was “possibly perfect”. It was shown that, at the aleatory level, the system pfd (probability of failure on demand) could be bounded above by the product of the pfd of channel A and the pnp (probability of non-perfection) of channel B. This result was presented as a way of avoiding the well-known difficulty that for two certainly-fallible channels, failures of the two will be dependent, i.e. the system pfd cannot be expressed simply as a product of the channel pfds. A price paid in this new approach for avoiding the issue of failure dependence is that the result is conservative. Furthermore, a complete analysis requires that account be taken of epistemic uncertainty – here concerning the numeric values of the two parameters pfdA and pnpB. Unfortunately this introduces a different difficult problem of dependence: estimating the dependence between an assessor’s beliefs about the parameters. The work reported here avoids this problem by obtaining results that require only an assessor’s marginal beliefs about the individual channels, i.e. they do not require knowledge of the dependence between these beliefs. The price paid is further conservatism in the results

An Empirical Comparison of Three Inference Methods

In this paper, an empirical evaluation of three inference methods for uncertain reasoning is presented in the context of Pathfinder, a large expert system for the diagnosis of lymph-node pathology. The inference procedures evaluated are (1) Bayes' theorem, assuming evidence is conditionally independent given each hypothesis; (2) odds-likelihood updating, assuming evidence is conditionally independent given each hypothesis and given the negation of each hypothesis; and (3) a inference method related to the Dempster-Shafer theory of belief. Both expert-rating and decision-theoretic metrics are used to compare the diagnostic accuracy of the inference methods.Comment: Appears in Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence (UAI1988

There Is No Pure Empirical Reasoning

The justificatory force of empirical reasoning always depends upon the existence of some synthetic, a priori justification. The reasoner must begin with justified, substantive constraints on both the prior probability of the conclusion and certain conditional probabilities; otherwise, all possible degrees of belief in the conclusion are left open given the premises. Such constraints cannot in general be empirically justified, on pain of infinite regress. Nor does subjective Bayesianism offer a way out for the empiricist. Despite often-cited convergence theorems, subjective Bayesians cannot hold that any empirical hypothesis is ever objectively justified in the relevant sense. Rationalism is thus the only alternative to an implausible skepticism

Complementary Lipschitz continuity results for the distribution of intersections or unions of independent random sets in finite discrete spaces

We prove that intersections and unions of independent random sets in finite spaces achieve a form of Lipschitz continuity. More precisely, given the distribution of a random set $\Xi$, the function mapping any random set distribution to the distribution of its intersection (under independence assumption) with $\Xi$ is Lipschitz continuous with unit Lipschitz constant if the space of random set distributions is endowed with a metric defined as the $L_k$ norm distance between inclusion functionals also known as commonalities. Moreover, the function mapping any random set distribution to the distribution of its union (under independence assumption) with $\Xi$ is Lipschitz continuous with unit Lipschitz constant if the space of random set distributions is endowed with a metric defined as the $L_k$ norm distance between hitting functionals also known as plausibilities. Using the epistemic random set interpretation of belief functions, we also discuss the ability of these distances to yield conflict measures. All the proofs in this paper are derived in the framework of Dempster-Shafer belief functions. Let alone the discussion on conflict measures, it is straightforward to transcribe the proofs into the general (non necessarily epistemic) random set terminology