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008			151027s2016 gw a s 001 0 eng d
020			_a9783319219219
024	7		_a10.1007/978-3-319-21921-9 _2doi
040			_aES-MaUEC
050		4	_aTK7872.D48 _bX89 2016 EB
082	0	4	_a629.8
100	1		_aXu, Yunfei _0local _997279 _0Local
245	1	0	_aBayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks : _bOnline Environmental Field Reconstruction in Space and Time _cby Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
250			_a1st ed.
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XII, 115 p.) _b43 ilustraciones, 2 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	1		_aSpringerBriefs in Electrical and Computer Engineering _x2191-8112
505	0		_aIntroduction -- Preliminaries -- Learning the Covariance Function -- Prediction with Known Covariance Function -- Fully Bayesian Approach -- Gaussian Process with Built-in Gaussian Markov Random Fields -- Bayesian Spatial Prediction Using Gaussian Markov Random Fields -- Conclusion.
520	3		_aThis brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive distribution of a scalar environmental field of interest. New techniques are introduced to avoid computationally prohibitive Markov chain Monte Carlo methods for resource-constrained mobile sensors. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks starts with a simple spatio-temporal model and increases the level of model flexibility and uncertainty step by step, simultaneously solving increasingly complicated problems and coping with increasing complexity, until it ends with fully Bayesian approaches that take into account a broad spectrum of uncertainties in observations, model parameters, and constraints in mobile sensor networks. The book is timely, being very useful for many researchers in control, robotics, computer science and statistics trying to tackle a variety of tasks such as environmental monitoring and adaptive sampling, surveillance, exploration, and plume tracking which are of increasing currency. Problems are solved creatively by seamless combination of theories and concepts from Bayesian statistics, mobile sensor networks, optimal experiment design, and distributed computation.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, EBSPRINGER
650	0	7	_aRedes de sensores inalámbricas _2embne _9441179
650		7	_aEstadística bayesiana _9160470 _0comprobar BNE20021966811 _2embne
650	0	4	_9496877 _aMecatrónica
650		7	_aRobótica _0comprobar BNE20022405562 _2embne _9160676
700	1		_aChoi, Jongeun _0local _997280 _0Local
700	1		_aDass, Sarat _0local _997281 _0Local
700	1		_aMaiti, Tapabrata _0local _997282 _0Local
830		0	_aSpringerBriefs in Electrical and Computer Engineering _x2191-8112 _9134065
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-21921-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319219219
907			_a.b12940823 _b04-11-17 _c21-11-16
998			_am _a_alco _a_vill _b04-11-17 _cm _dz _eo _feng _ggw _h0
945			_aTK7872.D48 X89 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11585237 _z06-04-17
999			_c84804 _d84804 _x1