Search results for "probabilistic"
showing 10 items of 380 documents
Probabilistic Logic under Coherence: Complexity and Algorithms
2005
In previous work [V. Biazzo, A. Gilio, T. Lukasiewicz and G. Sanfilippo, Probabilistic logic under coherence, model-theoretic probabilistic logic, and default reasoning in System P, Journal of Applied Non-Classical Logics 12(2) (2002) 189---213.], we have explored the relationship between probabilistic reasoning under coherence and model-theoretic probabilistic reasoning. In particular, we have shown that the notions of g-coherence and of g-coherent entailment in probabilistic reasoning under coherence can be expressed by combining notions in model-theoretic probabilistic reasoning with concepts from default reasoning. In this paper, we continue this line of research. Based on the above sem…
Constitutional Implications of electoral assumptions
2001
International audience
Inductive inference of recursive functions: complexity bounds
1991
This survey includes principal results on complexity of inductive inference for recursively enumerable classes of total recursive functions. Inductive inference is a process to find an algorithm from sample computations. In the case when the given class of functions is recursively enumerable it is easy to define a natural complexity measure for the inductive inference, namely, the worst-case mindchange number for the first n functions in the given class. Surely, the complexity depends not only on the class, but also on the numbering, i.e. which function is the first, which one is the second, etc. It turns out that, if the result of inference is Goedel number, then complexity of inference ma…
DAE-GP
2020
Estimation of distribution genetic programming (EDA-GP) algorithms are metaheuristics where sampling new solutions from a learned probabilistic model replaces the standard mutation and recombination operators of genetic programming (GP). This paper presents DAE-GP, a new EDA-GP which uses denoising autoencoder long short-term memory networks (DAE-LSTMs) as probabilistic model. DAE-LSTMs are artificial neural networks that first learn the properties of a parent population by mapping promising candidate solutions to a latent space and reconstructing the candidate solutions from the latent space. The trained model is then used to sample new offspring solutions. We show on a generalization of t…
A Strategy for the Prediction of the Response of Hysteretic Systems: A Base for Capacity Assessment of Buildings under Seismic Load
2014
A statistical non linearization method is used to approximate systems modeled by the Bouc differential equa- tion and excited by a Gaussian white noise external load. To this aim restricted potential models (RPM) are used, which are suitable for an extended number of nonlinear problems as have been proved several times. Since the solution of RPM is known by the probabilistic point of view, all statistical characteristics can be derived at once with advantages by the computational point of view. Hence, this paper discusses the possibility to determine sets of parameters characterizing po- tential models that are valid for describing a hysteretic behavior. In this way the characterization of …
A Simulation Analysis for Assessing the Reliability of AC/DC Hybrid Microgrids - Part II: Port Area and Residential Area
2021
This paper reports the second part of a simulation study with the aim of evaluating the ability of two portions of a hybrid AC/DC MV/LV network in maintaining their operation in off-grid mode during the loss of the main AC grid due to a failure. In particular, this paper follows a dual purpose: first, it analysis two microgrids in a residential area and a port zone capability of operating in islanded mode, applying a probabilistic approach, while there is different energy use cases, and second is to evaluate some reliability indicators.
Multi-modal Image Registration Using Fuzzy Kernel Regression
2009
This paper presents a study aimed to the realization of a novel multiresolution registration framework. The transformation function is computed iteratively as a composition of local deformations determined by the maximization of mutual information. At each iteration, local transformations are joint together using fuzzy kernel regression. This technique represents the core of the mothod and it's formally described from a probabilistic perspective. It avoids blocking artifacts and allows to keep the final deformation spatially congruent and smooth. Both qualitative and quantitative experimental results show that this approach is equally effective for registering datasets acquired from both si…
Long-Time Behaviour for the Brownian Heat Kernel on a Compact Riemannian Manifold and Bismut’s Integration-by-Parts Formula
2007
We give a probabilistic proof of the classical long-time behaviour of the heat kernel on a compact manifold by using Bismut’s integration-by-parts formula.
All-Possible-Couplings Approach to Measuring Probabilistic Context.
2013
From behavioral sciences to biology to quantum mechanics, one encounters situations where (i) a system outputs several random variables in response to several inputs, (ii) for each of these responses only some of the inputs may "directly" influence them, but (iii) other inputs provide a "context" for this response by influencing its probabilistic relations to other responses. These contextual influences are very different, say, in classical kinetic theory and in the entanglement paradigm of quantum mechanics, which are traditionally interpreted as representing different forms of physical determinism. One can mathematically construct systems with other types of contextuality, whether or not …
Approaching electrical tomography
2009
A general approach to electrical tomography is here described, based on the distribution of the experimental data to the set of voxels in which the subsoil has been divided. This approach utilizes the sensitivity coefficients as factors of the convolution procedure to execute the back projection of the data, to obtain the 3D pictures of the subsoil. A subsequent probabilistic filtering technique is described to improve the pictures in view of sharp boundary models. Some models are finally presented, mostly regarding cubic buried anomalies as well as pipe-shaped and L-shaped anomalies.