473 research outputs found
Computabilities of Validity and Satisfiability in Probability Logics over Finite and Countable Models
The -logic (which is called E-logic in this paper) of
Kuyper and Terwijn is a variant of first order logic with the same syntax, in
which the models are equipped with probability measures and in which the
quantifier is interpreted as "there exists a set of measure
such that for each , ...." Previously, Kuyper and
Terwijn proved that the general satisfiability and validity problems for this
logic are, i) for rational , respectively
-complete and -hard, and ii) for ,
respectively decidable and -complete. The adjective "general" here
means "uniformly over all languages."
We extend these results in the scenario of finite models. In particular, we
show that the problems of satisfiability by and validity over finite models in
E-logic are, i) for rational , respectively
- and -complete, and ii) for , respectively
decidable and -complete. Although partial results toward the countable
case are also achieved, the computability of E-logic over countable
models still remains largely unsolved. In addition, most of the results, of
this paper and of Kuyper and Terwijn, do not apply to individual languages with
a finite number of unary predicates. Reducing this requirement continues to be
a major point of research.
On the positive side, we derive the decidability of the corresponding
problems for monadic relational languages --- equality- and function-free
languages with finitely many unary and zero other predicates. This result holds
for all three of the unrestricted, the countable, and the finite model cases.
Applications in computational learning theory, weighted graphs, and neural
networks are discussed in the context of these decidability and undecidability
results.Comment: 47 pages, 4 tables. Comments welcome. Fixed errors found by Rutger
Kuype
Half-trek criterion for generic identifiability of linear structural equation models
A linear structural equation model relates random variables of interest and
corresponding Gaussian noise terms via a linear equation system. Each such
model can be represented by a mixed graph in which directed edges encode the
linear equations and bidirected edges indicate possible correlations among
noise terms. We study parameter identifiability in these models, that is, we
ask for conditions that ensure that the edge coefficients and correlations
appearing in a linear structural equation model can be uniquely recovered from
the covariance matrix of the associated distribution. We treat the case of
generic identifiability, where unique recovery is possible for almost every
choice of parameters. We give a new graphical condition that is sufficient for
generic identifiability and can be verified in time that is polynomial in the
size of the graph. It improves criteria from prior work and does not require
the directed part of the graph to be acyclic. We also develop a related
necessary condition and examine the "gap" between sufficient and necessary
conditions through simulations on graphs with 25 or 50 nodes, as well as
exhaustive algebraic computations for graphs with up to five nodes.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1012 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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