37,344 research outputs found
Price adjustment to news with uncertain precision
Bayesian learning provides the core concept of processing noisy information. In standard Bayesian frameworks, assessing the price impact of information requires perfect knowledge of news’ precision. In practice, however, precision is rarely dis- closed. Therefore, we extend standard Bayesian learning, suggesting traders infer news’ precision from magnitudes of surprises and from external sources. We show that interactions of the different precision signals may result in highly nonlinear price responses. Empirical tests based on intra-day T-bond futures price reactions to employment releases confirm the model’s predictions and show that the effects are statistically and economically significant
Updating beliefs with incomplete observations
Currently, there is renewed interest in the problem, raised by Shafer in
1985, of updating probabilities when observations are incomplete. This is a
fundamental problem in general, and of particular interest for Bayesian
networks. Recently, Grunwald and Halpern have shown that commonly used updating
strategies fail in this case, except under very special assumptions. In this
paper we propose a new method for updating probabilities with incomplete
observations. Our approach is deliberately conservative: we make no assumptions
about the so-called incompleteness mechanism that associates complete with
incomplete observations. We model our ignorance about this mechanism by a
vacuous lower prevision, a tool from the theory of imprecise probabilities, and
we use only coherence arguments to turn prior into posterior probabilities. In
general, this new approach to updating produces lower and upper posterior
probabilities and expectations, as well as partially determinate decisions.
This is a logical consequence of the existing ignorance about the
incompleteness mechanism. We apply the new approach to the problem of
classification of new evidence in probabilistic expert systems, where it leads
to a new, so-called conservative updating rule. In the special case of Bayesian
networks constructed using expert knowledge, we provide an exact algorithm for
classification based on our updating rule, which has linear-time complexity for
a class of networks wider than polytrees. This result is then extended to the
more general framework of credal networks, where computations are often much
harder than with Bayesian nets. Using an example, we show that our rule appears
to provide a solid basis for reliable updating with incomplete observations,
when no strong assumptions about the incompleteness mechanism are justified.Comment: Replaced with extended versio
Verification of Uncertain POMDPs Using Barrier Certificates
We consider a class of partially observable Markov decision processes
(POMDPs) with uncertain transition and/or observation probabilities. The
uncertainty takes the form of probability intervals. Such uncertain POMDPs can
be used, for example, to model autonomous agents with sensors with limited
accuracy, or agents undergoing a sudden component failure, or structural damage
[1]. Given an uncertain POMDP representation of the autonomous agent, our goal
is to propose a method for checking whether the system will satisfy an optimal
performance, while not violating a safety requirement (e.g. fuel level,
velocity, and etc.). To this end, we cast the POMDP problem into a switched
system scenario. We then take advantage of this switched system
characterization and propose a method based on barrier certificates for
optimality and/or safety verification. We then show that the verification task
can be carried out computationally by sum-of-squares programming. We illustrate
the efficacy of our method by applying it to a Mars rover exploration example.Comment: 8 pages, 4 figure
Radial Velocity Studies of Close Binary Stars.XIII
Radial-velocity measurements and sine-curve fits to the orbital radial
velocity variations are presented for ten close binary systems: EG Cep,V1191
Cyg, V1003 Her, BD+7_3142, V357 Peg, V407 Peg, V1123 Tau, V1128 Tau, HH UMa,
and PY Vir. While most of the studied eclipsing systems are contact binaries,
EG Cep is a detached or a semi-detached double-lined binary and V1003 Her is a
close binary of an uncertain type seen at a very low inclination angle. We
discovered two previously unknown triple systems, BD+7_3142 and PY Vir, both
with late spectral-type (K2V) binaries. Of interest is the low-mass ratio (q =
0.106) close binary V1191 Cyg showing an extremely fast period increase; the
system has a very short period for its spectral type and shows a W-type light
curve, a feature rather unexpected for such a low mass-ratio system.Comment: Accepted by AJ. 19 pages including 5 figure
Bayesian Learning for a Class of Priors with Prescribed Marginals
We present Bayesian updating of an imprecise probability measure, represented by a class of precise multidimensional probability measures. Choice and analysis of our class are motivated by expert interviews that we conducted with modelers in the context of climatic change. From the interviews we deduce that generically, experts hold a much more informed opinion on the marginals of uncertain parameters rather than on their correlations. Accordingly, we specify the class by prescribing precise measures for the marginals while letting the correlation structure subject to complete ignorance. For sake of transparency, our discussion focuses on the tutorial example of a linear two-dimensional Gaussian model. We operationalize Bayesian learning for that class by various updating rules, starting with (a modified version of) the generalized Bayes' rule and the maximum likelihood update rule (after Gilboa and Schmeidler). Over a large range of potential observations, the generalized Bayes' rule would provide non-informative results. We restrict this counter-intuitive and unnecessary growth of uncertainty by two means, the discussion of which refers to any kind of imprecise model, not only to our class. First, we find our class of priors too inclusive and, hence, require certain additional properties of prior measures in terms of smoothness of probability density functions. Second, we argue that both updating rules are dissatisfying, the generalized Bayes' rule being too conservative, i.e., too inclusive, the maximum likelihood rule being too exclusive. Instead, we introduce two new ways of Bayesian updating of imprecise probabilities: a ``weighted maximum likelihood method'' and a ``semi-classical method.'' The former bases Bayesian updating on the whole set of priors, however, with weighted influence of its members. By referring to the whole set, the weighted maximum likelihood method allows for more robust inferences than the standard maximum likelihood method and, hence, is better to justify than the latter.Furthermore, the semi-classical method is more objective than the weighted maximum likelihood method as it does not require the subjective definition of a weighting function. Both new methods reveal much more informative results than the generalized Bayes' rule, what we demonstrate for the example of a stylized insurance model
Price Adjustment to News with Uncertain Precision
Bayesian learning provides the core concept of processing noisy information. In standard Bayesian frameworks, assessing the price impact of information requires perfect knowledge of news’ precision. In practice, however, precision is rarely dis- closed. Therefore, we extend standard Bayesian learning, suggesting traders infer news’ precision from magnitudes of surprises and from external sources. We show that interactions of the different precision signals may result in highly nonlinear price responses. Empirical tests based on intra-day T-bond futures price reactions to employment releases confirm the model’s predictions and show that the effects are statistically and economically significant.Bayesian Learning, Macroeconomic Announcements, Information Quality, Precision Signals
Multi-Objective Approaches to Markov Decision Processes with Uncertain Transition Parameters
Markov decision processes (MDPs) are a popular model for performance analysis
and optimization of stochastic systems. The parameters of stochastic behavior
of MDPs are estimates from empirical observations of a system; their values are
not known precisely. Different types of MDPs with uncertain, imprecise or
bounded transition rates or probabilities and rewards exist in the literature.
Commonly, analysis of models with uncertainties amounts to searching for the
most robust policy which means that the goal is to generate a policy with the
greatest lower bound on performance (or, symmetrically, the lowest upper bound
on costs). However, hedging against an unlikely worst case may lead to losses
in other situations. In general, one is interested in policies that behave well
in all situations which results in a multi-objective view on decision making.
In this paper, we consider policies for the expected discounted reward
measure of MDPs with uncertain parameters. In particular, the approach is
defined for bounded-parameter MDPs (BMDPs) [8]. In this setting the worst, best
and average case performances of a policy are analyzed simultaneously, which
yields a multi-scenario multi-objective optimization problem. The paper
presents and evaluates approaches to compute the pure Pareto optimal policies
in the value vector space.Comment: 9 pages, 5 figures, preprint for VALUETOOLS 201
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