40,805 research outputs found
Probability Measures and projections on Quantum Logics
The present paper is devoted to modelling of a probability measure of logical
connectives on a quantum logic (QL), via a -map, which is a special map on
it. We follow the work in which the probability of logical conjunction,
disjunction and symmetric difference and their negations for non-compatible
propositions are studied.
We study such a -map on quantum logics, which is a probability measure
of a projection and show, that unlike classical (Boolean) logic, probability
measure of projections on a quantum logic are not necessarilly pure
projections.
We compare properties of a -map on QLs with properties of a probability
measure related to logical connectives on a Boolean algebra
A note on many valued quantum computational logics
The standard theory of quantum computation relies on the idea that the basic
information quantity is represented by a superposition of elements of the
canonical basis and the notion of probability naturally follows from the Born
rule. In this work we consider three valued quantum computational logics. More
specifically, we will focus on the Hilbert space C^3, we discuss extensions of
several gates to this space and, using the notion of effect probability, we
provide a characterization of its states.Comment: Pages 15, Soft Computing, 201
Non-classical conditional probability and the quantum no-cloning theorem
The quantum mechanical no-cloning theorem for pure states is generalized and
transfered to the quantum logics with a conditional probability calculus in a
rather abstract, though simple and basic fashion without relying on a tensor
product construction or finite dimension as required in other generalizations.Comment: 6 page
Belief as Willingness to Bet
We investigate modal logics of high probability having two unary modal
operators: an operator expressing probabilistic certainty and an operator
expressing probability exceeding a fixed rational threshold . Identifying knowledge with the former and belief with the latter, we may
think of as the agent's betting threshold, which leads to the motto "belief
is willingness to bet." The logic for has an
modality along with a sub-normal modality that extends
the minimal modal logic by way of four schemes relating
and , one of which is a complex scheme arising out of a theorem due to
Scott. Lenzen was the first to use Scott's theorem to show that a version of
this logic is sound and complete for the probability interpretation. We
reformulate Lenzen's results and present them here in a modern and accessible
form. In addition, we introduce a new epistemic neighborhood semantics that
will be more familiar to modern modal logicians. Using Scott's theorem, we
provide the Lenzen-derivative properties that must be imposed on finite
epistemic neighborhood models so as to guarantee the existence of a probability
measure respecting the neighborhood function in the appropriate way for
threshold . This yields a link between probabilistic and modal
neighborhood semantics that we hope will be of use in future work on modal
logics of qualitative probability. We leave open the question of which
properties must be imposed on finite epistemic neighborhood models so as to
guarantee existence of an appropriate probability measure for thresholds
.Comment: Removed date from v1 to avoid confusion on citation/reference,
otherwise identical to v
Characterizing and Reasoning about Probabilistic and Non-Probabilistic Expectation
Expectation is a central notion in probability theory. The notion of
expectation also makes sense for other notions of uncertainty. We introduce a
propositional logic for reasoning about expectation, where the semantics
depends on the underlying representation of uncertainty. We give sound and
complete axiomatizations for the logic in the case that the underlying
representation is (a) probability, (b) sets of probability measures, (c) belief
functions, and (d) possibility measures. We show that this logic is more
expressive than the corresponding logic for reasoning about likelihood in the
case of sets of probability measures, but equi-expressive in the case of
probability, belief, and possibility. Finally, we show that satisfiability for
these logics is NP-complete, no harder than satisfiability for propositional
logic.Comment: To appear in Journal of the AC
Formal verification of higher-order probabilistic programs
Probabilistic programming provides a convenient lingua franca for writing
succinct and rigorous descriptions of probabilistic models and inference tasks.
Several probabilistic programming languages, including Anglican, Church or
Hakaru, derive their expressiveness from a powerful combination of continuous
distributions, conditioning, and higher-order functions. Although very
important for practical applications, these combined features raise fundamental
challenges for program semantics and verification. Several recent works offer
promising answers to these challenges, but their primary focus is on semantical
issues.
In this paper, we take a step further and we develop a set of program logics,
named PPV, for proving properties of programs written in an expressive
probabilistic higher-order language with continuous distributions and operators
for conditioning distributions by real-valued functions. Pleasingly, our
program logics retain the comfortable reasoning style of informal proofs thanks
to carefully selected axiomatizations of key results from probability theory.
The versatility of our logics is illustrated through the formal verification of
several intricate examples from statistics, probabilistic inference, and
machine learning. We further show the expressiveness of our logics by giving
sound embeddings of existing logics. In particular, we do this in a parametric
way by showing how the semantics idea of (unary and relational) TT-lifting can
be internalized in our logics. The soundness of PPV follows by interpreting
programs and assertions in quasi-Borel spaces (QBS), a recently proposed
variant of Borel spaces with a good structure for interpreting higher order
probabilistic programs
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