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You searched for subject:(Likelihood finding). Showing records 1 – 2 of 2 total matches.

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1. Ben Mrad, Ali. Observations probabilistes dans les réseaux bayésiens : Probabilistic evidence in bayesian networks.

Degree: Docteur es, Informatique, 2015, Valenciennes; École nationale d'Ingénieurs de Sfax (Tunisie)

Dans un réseau bayésien, une observation sur une variable signifie en général que cette variable est instanciée. Ceci signifie que l’observateur peut affirmer avec certitude que la variable est dans l’état signalé. Cette thèse porte sur d’autres types d’observations, souvent appelées observations incertaines, qui ne peuvent pas être représentées par la simple affectation de la variable. Cette thèse clarifie et étudie les différents concepts d’observations incertaines et propose différentes applications des observations incertaines dans les réseaux bayésiens.Nous commençons par dresser un état des lieux sur les observations incertaines dans les réseaux bayésiens dans la littérature et dans les logiciels, en termes de terminologie, de définition, de spécification et de propagation. Il en ressort que le vocabulaire n'est pas clairement établi et que les définitions proposées couvrent parfois des notions différentes.Nous identifions trois types d’observations incertaines dans les réseaux bayésiens et nous proposons la terminologie suivante : observation de vraisemblance, observation probabiliste fixe et observation probabiliste non-fixe. Nous exposons ensuite la façon dont ces observations peuvent être traitées et propagées.Enfin, nous donnons plusieurs exemples d’utilisation des observations probabilistes fixes dans les réseaux bayésiens. Le premier exemple concerne la propagation d'observations sur une sous-population, appliquée aux systèmes d'information géographique. Le second exemple concerne une organisation de plusieurs agents équipés d'un réseau bayésien local et qui doivent collaborer pour résoudre un problème. Le troisième exemple concerne la prise en compte d'observations sur des variables continues dans un RB discret. Pour cela, l'algorithme BN-IPFP-1 a été implémenté et utilisé sur des données médicales de l'hôpital Bourguiba de Sfax.

In a Bayesian network, evidence on a variable usually signifies that this variable is instantiated, meaning that the observer can affirm with certainty that the variable is in the signaled state. This thesis focuses on other types of evidence, often called uncertain evidence, which cannot be represented by the simple assignment of the variables. This thesis clarifies and studies different concepts of uncertain evidence in a Bayesian network and offers various applications of uncertain evidence in Bayesian networks.Firstly, we present a review of uncertain evidence in Bayesian networks in terms of terminology, definition, specification and propagation. It shows that the vocabulary is not clear and that some terms are used to represent different concepts.We identify three types of uncertain evidence in Bayesian networks and we propose the followingterminology: likelihood evidence, fixed probabilistic evidence and not-fixed probabilistic evidence. We define them and describe updating algorithms for the propagation of uncertain evidence. Finally, we propose several examples of the use of fixed probabilistic evidence in Bayesian networks. The first example concerns evidence on a…

Advisors/Committee Members: Piechowiak, Sylvain (thesis director), Abid, Mohamed (thesis director), Delcroix, Véronique (thesis director).

Subjects/Keywords: Intelligence artificielle; Incertitude; Modèle graphique probabiliste; Réseau bayésien; Observation; Observation incertaine; Observation probabiliste; Observation de vraisemblance.; Artificial intelligence; Uncertainty; Probabilistic graphical models; Bayesian network; Evidence; Uncertain evidence; Probabilistic evidence; Likelihood finding; Soft evidence; Virtual evidence.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ben Mrad, A. (2015). Observations probabilistes dans les réseaux bayésiens : Probabilistic evidence in bayesian networks. (Doctoral Dissertation). Valenciennes; École nationale d'Ingénieurs de Sfax (Tunisie). Retrieved from http://www.theses.fr/2015VALE0018

Chicago Manual of Style (16th Edition):

Ben Mrad, Ali. “Observations probabilistes dans les réseaux bayésiens : Probabilistic evidence in bayesian networks.” 2015. Doctoral Dissertation, Valenciennes; École nationale d'Ingénieurs de Sfax (Tunisie). Accessed October 22, 2019. http://www.theses.fr/2015VALE0018.

MLA Handbook (7th Edition):

Ben Mrad, Ali. “Observations probabilistes dans les réseaux bayésiens : Probabilistic evidence in bayesian networks.” 2015. Web. 22 Oct 2019.

Vancouver:

Ben Mrad A. Observations probabilistes dans les réseaux bayésiens : Probabilistic evidence in bayesian networks. [Internet] [Doctoral dissertation]. Valenciennes; École nationale d'Ingénieurs de Sfax (Tunisie); 2015. [cited 2019 Oct 22]. Available from: http://www.theses.fr/2015VALE0018.

Council of Science Editors:

Ben Mrad A. Observations probabilistes dans les réseaux bayésiens : Probabilistic evidence in bayesian networks. [Doctoral Dissertation]. Valenciennes; École nationale d'Ingénieurs de Sfax (Tunisie); 2015. Available from: http://www.theses.fr/2015VALE0018

2. Trotter, Matthew. Range finding in passive wireless sensor networks using power-optimized waveforms.

Degree: PhD, Electrical and Computer Engineering, 2011, Georgia Tech

Passive wireless sensor networks (WSNs) are quickly becoming popular for many applications such as article tracking, position location, temperature sensing, and passive data storage. Passive tags and sensors are unique in that they collect their electrical energy by harvesting it from the ambient environment. Tags with charge pumps collect their energy from the signal they receive from the transmitting source. The efficiency of converting the received signal to DC power is greatly enhanced using a power-optimized waveform (POW). Measurements in the first part of this dissertation show that a POW can provide efficiency gains of up to 12 dB compared to a sine-wave input. Tracking the real-time location of these passive tags is a specialized feature used in some applications such as animal tracking. A passive WSN that uses POWs for the improvement of energy-harvesting may also estimate the range to a tag by measuring the time delay of propagation from the transmitter to the tag and back to the transmitter. The maximum-likelihood (ML) estimator is used for estimating this time delay, which simplifies to taking the cross-correlation of the received signal with the transmitted signal. This research characterizes key aspects of performing range estimations in passive WSNs using POWs. The shape of the POW has a directly-measurable effect on ranging performance. Measurements and simulations show that the RMS bandwidth of the waveform has an inversely proportional relationship to the uncertainty of a range measurement. The clutter of an environment greatly affects the uncertainty and bias exhibited by a range estimator. Random frequency-selective environments with heavy clutter are shown to produce estimation uncertainties more than 20 dB higher than the theoretical lower bound. Estimation in random frequency-flat environments is well-behaved and fits the theory quite nicely. Nonlinear circuits such as the charge pump distort the POW during reflection, which biases the range estimations. This research derives an empirical model for predicting the estimation bias for Dickson charge pumps and verifies it with simulations and measurements. Advisors/Committee Members: Durgin, Gregory (Committee Chair), Ingram, Mary Ann (Committee Member), Patwari, Neal (Committee Member), Peterson, Andrew (Committee Member), Richards, Mark (Committee Member).

Subjects/Keywords: Maximum likelihood; Wireless sensor networks; Time delay estimation; Range estimation; Ranging; Energy harvesting; Charge pumps; Range finding; Power optimized waveforms; Wireless sensor networks; Range-finding; Passive components

…Measured performance of a maximum likelihood estimator using a Gaussian POW with BRMS = 10 MHz on… …140 Measured performance of a maximum likelihood estimator using a Gaussian POW with BRMS… …141 Measured performance of a maximum likelihood estimator using a Gaussian POW with BRMS… …142 Measured performance of a maximum likelihood estimator using a Square POW with BRMS… …143 Measured performance of a maximum likelihood estimator using a Square POW with BRMS… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Trotter, M. (2011). Range finding in passive wireless sensor networks using power-optimized waveforms. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/42916

Chicago Manual of Style (16th Edition):

Trotter, Matthew. “Range finding in passive wireless sensor networks using power-optimized waveforms.” 2011. Doctoral Dissertation, Georgia Tech. Accessed October 22, 2019. http://hdl.handle.net/1853/42916.

MLA Handbook (7th Edition):

Trotter, Matthew. “Range finding in passive wireless sensor networks using power-optimized waveforms.” 2011. Web. 22 Oct 2019.

Vancouver:

Trotter M. Range finding in passive wireless sensor networks using power-optimized waveforms. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/1853/42916.

Council of Science Editors:

Trotter M. Range finding in passive wireless sensor networks using power-optimized waveforms. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/42916

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