11,378 research outputs found
Uniqueness of the Level Two Bayesian Network Representing a Probability Distribution
Bayesian Networks are graphic probabilistic models through
which we can acquire, capitalize on, and exploit knowledge. they are becoming
an important tool for research and applications in artificial intelligence
and many other fields in the last decade. This paper presents
Bayesian networks and discusses the inference problem in such models. It
proposes a statement of the problem and the proposed method to compute
probability distributions. It also uses D-separation for simplifying
the computation of probabilities in Bayesian networks. Given a Bayesian
network over a family of random variables, this paper presents a result
on the computation of the probability distribution of a subset of
using separately a computation algorithm and D-separation properties.
It also shows the uniqueness of the obtained result
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
We propose a logical/mathematical framework for statistical parameter
learning of parameterized logic programs, i.e. definite clause programs
containing probabilistic facts with a parameterized distribution. It extends
the traditional least Herbrand model semantics in logic programming to
distribution semantics, possible world semantics with a probability
distribution which is unconditionally applicable to arbitrary logic programs
including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM
algorithm, the graphical EM algorithm, that runs for a class of parameterized
logic programs representing sequential decision processes where each decision
is exclusive and independent. It runs on a new data structure called support
graphs describing the logical relationship between observations and their
explanations, and learns parameters by computing inside and outside probability
generalized for logic programs. The complexity analysis shows that when
combined with OLDT search for all explanations for observations, the graphical
EM algorithm, despite its generality, has the same time complexity as existing
EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside
algorithm for PCFGs, and the one for singly connected Bayesian networks that
have been developed independently in each research field. Learning experiments
with PCFGs using two corpora of moderate size indicate that the graphical EM
algorithm can significantly outperform the Inside-Outside algorithm
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model
parameter that is only observed indirectly through noisy data in an ill-posed
inverse problem. A key aspect is to formalize the steps of reconstruction and
task as appropriate estimators (non-randomized decision rules) in statistical
estimation problems. The implementation makes use of (deep) neural networks to
provide a differentiable parametrization of the family of estimators for both
steps. These networks are combined and jointly trained against suitable
supervised training data in order to minimize a joint differentiable loss
function, resulting in an end-to-end task adapted reconstruction method. The
suggested framework is generic, yet adaptable, with a plug-and-play structure
for adjusting both the inverse problem and the task at hand. More precisely,
the data model (forward operator and statistical model of the noise) associated
with the inverse problem is exchangeable, e.g., by using neural network
architecture given by a learned iterative method. Furthermore, any task that is
encodable as a trainable neural network can be used. The approach is
demonstrated on joint tomographic image reconstruction, classification and
joint tomographic image reconstruction segmentation
The Bayesian Formulation of EIT: Analysis and Algorithms
We provide a rigorous Bayesian formulation of the EIT problem in an infinite
dimensional setting, leading to well-posedness in the Hellinger metric with
respect to the data. We focus particularly on the reconstruction of binary
fields where the interface between different media is the primary unknown. We
consider three different prior models - log-Gaussian, star-shaped and level
set. Numerical simulations based on the implementation of MCMC are performed,
illustrating the advantages and disadvantages of each type of prior in the
reconstruction, in the case where the true conductivity is a binary field, and
exhibiting the properties of the resulting posterior distribution.Comment: 30 pages, 10 figure
Social status in a social structure: noisy signaling in networks
How do incentives to engage in costly signaling depend on social structure? This paper formalises and extends Thorstein Veblen’s theory of how costly signaling by conspicuous consumption depends on social structure. A noisy signaling game is introduced in which spectators observe signals only imperfectly, and use Bayesian updating to interpret the observed signals. It is shown that this noisy signaling game has (under some weak regularity conditions) a unique plausible Perfect Bayesian Nash equilibrium. Then, a social information network is introduced as a second source of information about a player’s type. Equilibrium signaling depends in the resulting game on the relative quality of the substitute sources of information, which depends again on the social network. For some highly stylised networks, the dependence of equilibrium costly signaling on network characteristics (network size, density and connectedness, the centrality of the consumer in the network) is studied, and a simple dominance result for more arbitrary networks is suggested.
A probabilistic model for information and sensor validation
This paper develops a new theory and model for information and sensor validation. The model represents relationships between variables using Bayesian networks and utilizes probabilistic propagation to estimate the expected values of variables. If the estimated value of a variable differs from the actual value, an apparent fault is detected. The fault is only apparent since it may be that the estimated value is itself based on faulty data. The theory extends our understanding of when it is possible to isolate real faults from potential faults and supports the development of an algorithm that is capable of isolating real faults without deferring the problem to the use of expert provided domain-specific rules. To enable practical adoption for real-time processes, an any time version of the algorithm is developed, that, unlike most other algorithms, is capable of returning improving assessments of the validity of the sensors as it accumulates more evidence with time. The developed model is tested by applying it to the validation of temperature sensors during the start-up phase of a gas turbine when conditions are not stable; a problem that is known to be challenging. The paper concludes with a discussion of the practical applicability and scalability of the model
Securing Databases from Probabilistic Inference
Databases can leak confidential information when users combine query results
with probabilistic data dependencies and prior knowledge. Current research
offers mechanisms that either handle a limited class of dependencies or lack
tractable enforcement algorithms. We propose a foundation for Database
Inference Control based on ProbLog, a probabilistic logic programming language.
We leverage this foundation to develop Angerona, a provably secure enforcement
mechanism that prevents information leakage in the presence of probabilistic
dependencies. We then provide a tractable inference algorithm for a practically
relevant fragment of ProbLog. We empirically evaluate Angerona's performance
showing that it scales to relevant security-critical problems.Comment: A short version of this paper has been accepted at the 30th IEEE
Computer Security Foundations Symposium (CSF 2017
- …