Search results for "Probabilistic"
showing 10 items of 380 documents
Accounting for soil parameter uncertainty in a physically based and distributed approach for rainfall-triggered landslides
2015
In this study we propose a probabilistic approach for coupled distributed hydrological-hillslope stability models that accounts for soil parameters uncertainty at basin scale. The geotechnical and soil retention curve parameters are treated as random variables across the basin and theoretical probability distributions of the Factor of Safety (FS) are estimated. The derived distributions are used to obtain the spatio-temporal dynamics of probability of failure, in terms of parameters uncertainty, conditioned to soil moisture dynamics. The framework has been implemented in the tRIBS-VEGGIE (Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator-VEGetation Generator fo…
Simplified Probabilistic-Topologic Model for Reproducing Hillslope Rill Network Surface Runoff
2015
AbstractThis work presents a simplified probabilistic-topologic model for reproducing rill network surface runoff on a square-plane hillslope. The model requires only two parameters: the first is related to the production capacity of overland flow of the hillslope, at the initial conditions of the process, and the second depends on the sinuosity of the rill network. From a hydrological point of view, the following parameters account for the effects that essentially delineate the hydrologic response of a natural hillslope: rainfall intensity, hillslope roughness, and slope. Obviously, the reliability of the model is pending experimental validation that has only just begun. However, a prelimi…
Identification of parameters of the Jiles-Atherton model by neural networks
2011
In this paper a procedure for the identification of the parameters of the Jiles–Atherton (JA) model is presented. The parameters of the JA model of a material are found by using a neural network trained by a collection of hysteresis curves, whose parameters are known. After a presentation of the Jiles–Atherton model, the neural network and the training procedure are described and the method is validated by using some numerical, as well as experimental, data.
INDUCTIVE INFERENCE OF LIMITING PROGRAMS WITH BOUNDED NUMBER OF MIND CHANGES
1996
We consider inductive inference of total recursive functions in the case, when produced hypotheses are allowed some finite number of times to change “their mind” about each value of identifiable function. Such type of identification, which we call inductive inference of limiting programs with bounded number of mind changes, by its power lies somewhere between the traditional criteria of inductive inference and recently introduced inference of limiting programs. We consider such model of inductive inference for EX and BC types of identification, and we study • tradeoffs between the number of allowed mind changes and the number of anomalies, and • relations between classes of functions ident…
Inverted and mirror repeats in model nucleotide sequences.
2007
We analytically and numerically study the probabilistic properties of inverted and mirror repeats in model sequences of nucleic acids. We consider both perfect and non-perfect repeats, i.e. repeats with mismatches and gaps. The considered sequence models are independent identically distributed (i.i.d.) sequences, Markov processes and long range sequences. We show that the number of repeats in correlated sequences is significantly larger than in i.i.d. sequences and that this discrepancy increases exponentially with the repeat length for long range sequences.
Probabilistic inferences from conjoined to iterated conditionals
2017
Abstract There is wide support in logic, philosophy, and psychology for the hypothesis that the probability of the indicative conditional of natural language, P ( if A then B ) , is the conditional probability of B given A, P ( B | A ) . We identify a conditional which is such that P ( if A then B ) = P ( B | A ) with de Finetti's conditional event, B | A . An objection to making this identification in the past was that it appeared unclear how to form compounds and iterations of conditional events. In this paper, we illustrate how to overcome this objection with a probabilistic analysis, based on coherence, of these compounds and iterations. We interpret the compounds and iterations as cond…
Probabilistic quantum clustering
2020
Abstract Quantum Clustering is a powerful method to detect clusters with complex shapes. However, it is very sensitive to a length parameter that controls the shape of the Gaussian kernel associated with a wave function, which is employed in the Schrodinger equation with the role of a density estimator. In addition, linking data points into clusters requires local estimates of covariance which requires further parameters. This paper proposes a Bayesian framework that provides an objective measure of goodness-of-fit to the data, to optimise the adjustable parameters. This also quantifies the probabilities of cluster membership, thus partitioning the data into a specific number of clusters, w…
Discovery privacy threats via device de-anonymization in LoRaWAN
2021
LoRaWAN (Long Range WAN) is one of the well-known emerging technologies for the Internet of Things (IoT). Many IoT applications involve simple devices that transmit their data toward network gateways or access points that, in their turn, redirect data to application servers. While several security issues have been addressed in the LoRaWAN specification v1.1, there are still some aspects that may undermine privacy and security of the interconnected IoT devices. In this paper, we tackle a privacy aspect related to LoRaWAN device identity. The proposed approach, by monitoring the network traffic in LoRaWAN, is able to derive, in a probabilistic way, the unique identifier of the IoT device from…
Explanatory Reasoning: A Probabilistic Interpretation
2016
This paper deals with inference guided by explanatory considerations –specifically with the prospects for a probabilistic interpretation of it. After pointing out some differences between two sorts of explanatory reasoning – i.e.: abduction and “inference to the best explanation” – in the first section I distinguish two tasks: (a) to discern which explanation is the best one; (b) to assess whether the best explanation deserves to be legitimately believed. In Sect. 20.2 I discuss some recent definitions of explanatory power based on “reduction of uncertainty” (Schupbach and Sprenger 2011; Crupi and Tentori 2012). Even though a probabilistic framework is a promising option here, I will argue …
Fuzzy Logic, Knowledge and Natural Language
2012
This is an introductive study on what Fuzzy Logic is, on the difference between Fuzzy Logic and the other many-valued calculi and on the possible relationship between Fuzzy Logic and the complex sciences. Fuzzy Logic is nowadays a very popular logic methodology. Different kinds of applications in cybernetics, in software programming and its growing use in medicine seems to make Fuzzy Logic, according to someone, the “new” logic of science and technology. In his enthusiastic panegyric of Fuzzy Logic, Kosko (1993) argues that after thirty years from the birth of this calculus, it is time to declare the new era of Fuzzy Logic and to forget the old era of classical logic. I think that this poin…