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1. Applied predictive modeling
by Kuhn, Max and Johnson, Kjell
2013, ISBN 1461468485, xiii, 600 pages
Prediction theory | Mathematical models | R (Computer program language) | Mathematical statistics | Statistics
Book
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2. Probabilistic graphical models for genetics, genomics, and postgenomics
by Sinoquet, Christine, editor and Mourad, Raphael
2014, First edition., ISBN 0198709021, xxvii, 449 pages, 4 unnumbered pages of color plates
Genetics | Mathematical models | Biomathematics and Statistics | Probability and Statistics | Genomics | Statistical methods | Graphical modeling (Statistics) | Life Sciences | Human health and pathology | Quantitative Methods | Vegetal Biology | Statistics | Machine Learning
Book
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3. The chemical bond in inorganic chemistry
: the bond valence model
by Brown, I. David (Ian David)
2002, International Union of Crystallography monographs on crystallography, ISBN 9780198508700, Volume 12, x, 278
Chemical bonds | Mathematical models | Inorganic compounds | Chemistry, Inorganic
Book
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4. The global forest products model
: structure, estimation, and applications
by Buongiorno, Joseph
2003, ISBN 0121413624, xv, 301
Forest products industry
Book
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5. Full Text The PESERA coarse scale erosion model for Europe. I. – Model rationale and implementation
by Kirkby, M. J and Irvine, B. J and Jones, R. J. A and Govers, G and PESERA Team and PESERA team
European journal of soil science, ISSN 1351-0754, 12/2008, Volume 59, Issue 6, pp. 1293 - 1306
Soil Science | Agriculture | Life Sciences & Biomedicine | Science & Technology | Fundamental and applied biological sciences. Psychology | Engineering and environment geology. Geothermics | Exact sciences and technology | Earth, ocean, space | Earth sciences | Biological and medical sciences | Natural hazards: prediction, damages, etc | Soil science | Agronomy. Soil science and plant productions | Soil erosion, conservation, land management and development | Geography | Hydrologic cycle | Soil moisture | Runoff | Analysis | Rain and rainfall | Erosion | River channels | Sediment transport | Sciences of the Universe | Continental interfaces, environment | Environmental Sciences | Global Changes
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6. Full Text Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model
by Yuan, Chaoqing and Liu, Sifeng and Fang, Zhigeng
Energy (Oxford), ISSN 0360-5442, 04/2016, Volume 100, pp. 384 - 390
Energy consumption | ARIMA model | GM(1,1) model | Prediction | Thermodynamics | Physical Sciences | Technology | Energy & Fuels | Science & Technology | Forecasts and trends | Comparative analysis | Energy industry | Sustainable development | Residues | Statistical analysis | Fluctuation | Markets | Mathematical models | Trends | Autoregressive processes
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7. Full Text A novel energy consumption forecasting model combining an optimized DGM (1, 1) model with interval grey numbers
by Ye, Jing and Dang, Yaoguo and Ding, Song and Yang, Yingjie
Journal of cleaner production, ISSN 0959-6526, 08/2019, Volume 229, pp. 256 - 267
Energy consumption | Grey system theory | Electricity | Interval number | Prediction | Engineering | Green & Sustainable Science & Technology | Engineering, Environmental | Life Sciences & Biomedicine | Technology | Science & Technology - Other Topics | Environmental Sciences & Ecology | Science & Technology | Environmental Sciences | Energy use | Business schools | Analysis | Energy policy | Models | Forecasts and trends | Sustainable development
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8. Full Text Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding
by van Eeuwijk, Fred A and Bustos-Korts, Daniela and Millet, Emilie J and Boer, Martin P and Kruijer, Willem and Thompson, Addie and Malosetti, Marcos and Iwata, Hiroyoshi and Quiroz, Roberto and Kuppe, Christian and Muller, Onno and Blazakis, Konstantinos N and Yu, Kang and Tardieu, Francois and Chapman, Scott C
Plant science (Limerick), ISSN 0168-9452, 05/2019, Volume 282, pp. 23 - 39
Statistical genetics | Prediction | Multi-trait model | Reaction norm | Phenotyping platform | Crop growth model | Genotype-by-environment-interaction | Multi-environment model | Genomic prediction | Plant breeding | Mixed model | Genotype-to-phenotype model | Phenotyping | Response surface | Physiology | Quantitative genetics | Plant physiology | Analysis | Genomics | Physiological aspects | Models | Methods | Genetic polymorphisms | Index Medicus | Life Sciences | Vegetal Biology
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9. Full Text All models are wrong...': an introduction to model uncertainty
by Wit, Ernst and van den Heuvel, Edwin and Romeijn, Jan-Willem
Statistica Neerlandica, ISSN 0039-0402, 08/2012, Volume 66, Issue 3, pp. 217 - 236
model uncertainty | questionnaire | frequentist | SIMPLICITY | Bayesian | model selection | foundations | confirmation theory | SELECTION | PREDICTION | Confirmation theory | Foundations | Questionnaire | Frequentist | Model selection | Model uncertainty | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Uncertainty | Statistical analysis | Bayesian analysis
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10. The CERT resilience management model
: a maturity model for managing operational resilience
by Caralli, Richard A and White, David W. 1964- (David Warren) and Allen, Julia H and Carnegie-Mellon University. CERT Coordination Center
2011, The SEI series in software engineering, ISBN 9780321712431, xxiv, 1029
Crisis management | Organizational effectiveness | Success in business | Resilience (Personality trait) | Organizational change
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