Search Results - mathematical statistics data processing.

Volcanoes : formation, eruptions and modelling

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Handbook of operations research in natural resources

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Phase equilibria, phase diagrams and phase transformations : their thermodynamic basis by Hillert, Mats, 1924-

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Theoretical foundations of digital imaging using MATLAB by I︠A︡roslavskiĭ, L. P. (Leonid Pinkhusovich)

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Quantitative ecotoxicology by Newman, Michael C.

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Geometric Morphometrics for Biologists. by Zelditch, Miriam Leah

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Biostatistics for Epidemiology and Public Health Using R. by Chan, Bertram K. C., PhD

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Matlab in quality assurance sciences by Burstein, Leonid

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Improving Information for Social Policy Decisions -- The Uses of Microsimulation Modeling : Volume I, Review and Recommendations.

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Practice Issues in HIV/AIDS Services : Empowerment-Based Models and Program Applications.

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Spatial capture-recapture

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Computational models of brain and behavior

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Predicting the remaining economic life of wastewater pipes ; Phase 1 development of standard data structure to support wastewater pipe condition and performance prediction by Sinha, Sunil K.

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Difference and differential equations with applications in queueing theory by Haghighi, Aliakbar Montazer

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Proceedings of Coastal Dynamics 2009 : impacts of human activities on dynamic coastal processes ; Tokyo, Japan, 7-11 September 2009

Table of Contents: “…Seasonal closure of Chilaw Inlet, Sri Lanka : physical processes and mathematical modeling / Magnus Larson [and others] -- 98. …”
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Healthcare operations management by McLaughlin, Daniel B., 1945-, Olson, John R. (Professor)

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Official guide for GMAT quantitative review, 2015

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Advances in heavy tailed risk modeling : a handbook of operational risk by Peters, Gareth W., 1978-

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Mixtures : estimation and applications

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Remote Sensing Digital Image Analysis : an Introduction by Richards, John A.

Table of Contents: “…More Advanced Considerations -- 8.8 Context Classification -- 8.8.1 The Concept of Spatial Context -- 8.8.2 Context Classification by Image Pre-Processing -- 8.8.3 Post Classification Filtering -- 8.8.4 Probabilistic Label Relaxation -- 8.8.4.1 The Basic Algorithm -- 8.8.4.2 The Neighbourhood Function -- 8.8.4.3 Determining the Compatibility Coefficients -- 8.8.4.4 The Final Step -- Stopping the Process -- 8.8.4.5 Examples -- 8.9 Classification of Mixed Image Data -- 8.9.1 The Stacked Vector Approach -- 8.9.2 Statistical Methods -- 8.9.3 The Theory of Evidence -- 8.9.3.1 The Concept of Evidential Mass -- 8.9.3.2 Combining Evidence -- the Orthogonal Sum -- 8.9.3.3 Decision Rule -- 8.10 Classification Using Neural Networks -- 8.10.1 Linear Discrimination -- 8.10.1.1 Concept of a Weight Vector -- 8.10.1.2 Testing Class Membership -- 8.10.1.3 Training -- 8.10.1.4 Setting the Correction Increment -- 8.10.1.5 Classification -- The Threshold Logic Unit -- 8.10.1.6 Multicategory Classification -- 8.10.2 Networks of Classifiers -- Solutions of Nonlinear Problems -- 8.10.3 The Neural Network Approach -- 8.10.3.1 The Processing Element -- 8.10.3.2 Training the Neural Network -- Backpropagation -- 8.10.3.3 Choosing the Network Parameters -- 8.10.3.4 Examples -- References for Chapter 8 -- Problems -- 9 -- Clustering and Unsupervised Classification -- 9.1 Delineation of Spectral Classes -- 9.2 Similarity Metrics and Clustering Criteria -- 9.3 The Iterative Optimization (Migrating Means) Clustering Algorithm -- 9.3.1 The Basic.…”
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