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Data Fitting and Uncertainty

logical and accessible fashion. The primary goal of the text is to explain how data fitting via least squares works. The reader will find that the;

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Uncertainty, Calibration and Probability

errors and curve fitting with numerous examples of uncertainty case studies. Many useful tables and computational formulae are included as well;

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Using R for Modelling and Quantitative Methods in Fisheries

Estimation provides a detailed explanation of the requirements and steps involved in fitting models to data, using R and, mainly, maximum likelihood;

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Using R for Modelling and Quantitative Methods in Fisheries

Estimation provides a detailed explanation of the requirements and steps involved in fitting models to data, using R and, mainly, maximum likelihood;

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Practical Handbook of Curve Fitting

experience in various disciplines and activities related to curve fitting. The book demonstrates how to analyze World data bases and graph and map;

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Practical Handbook of Curve Fitting

experience in various disciplines and activities related to curve fitting. The book demonstrates how to analyze World data bases and graph and map;

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Modeling Microbial Responses in Food

improvements in the development of primary and secondary models, statistical fitting strategies, and novel data collection methods. An;

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Modeling Microbial Responses in Food

improvements in the development of primary and secondary models, statistical fitting strategies, and novel data collection methods. An;

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Least Squares Data Fitting with Applications

examples that illustrate the techniques and algorithms. Least Squares Data Fitting with Applications can be used as a textbook for advanced;

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Uncertainty Modelling and Quality Control for Spatial Data

settlement Uncertainty Modelling and Quality Control for Spatial Data serves university students, researchers and professionals;

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Uncertainty Modelling and Quality Control for Spatial Data

settlement Uncertainty Modelling and Quality Control for Spatial Data serves university students, researchers and professionals;

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Uncertainty Modeling for Data Mining

Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise;

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Probabilistic Ranking Techniques in Relational Databases

new processing techniques leveraging the capabilities of relational database systems to recognize and handle data uncertainty in score-based;

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Probabilistic Ranking Techniques in Relational Databases

new processing techniques leveraging the capabilities of relational database systems to recognize and handle data uncertainty in score-based;

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Beyond Traditional Probabilistic Data Processing Techniques

interested in data processing under uncertainty, and an introduction to the latest trends and techniques in this area, suitable for graduate students.;

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Spatial Data Quality

quality, leading to inaccurate results, uncertainty, error propagation, and potentially legal liability. Spatial data quality has become an;

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Uncertain Schema Matching

, distributed data sources. Schema matching is one of the basic operations required by the process of data and schema integration, and thus has a great;

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Bayesian Networks Handbook

probabilistic semantics; and lastly, this method along with statistical method provides efficient and balanced approach to avoid over fitting of data;

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Uncertainty Management in Remote Sensing of Climate Data

challenges in data analysis. Uncertainty characterization is needed for every satellite mission and scientists continue to be challenged by the need;

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Fitting Statistical Distributions

Throughout the physical and social sciences, researchers face the challenge of fitting statistical distributions to their data. Although;

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Fitting Statistical Distributions

Throughout the physical and social sciences, researchers face the challenge of fitting statistical distributions to their data. Although;

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An Introduction to Mathematical Epidemiology

model building, fitting to data, local and global analysis techniques. Various types of deterministic dynamical models are considered: ordinary;

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Scalable Uncertainty Management

this data and knowledge. The International Conference on Scalable Uncertainty (SUM) aims to provide a forum for researchers who are working on;

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Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations

time series; discrete models and computational intelligence; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy logic;

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Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses

science (GISci) is the new kid on the block. Its theoretical foundations are therefore still developing and data quality and uncertainty modeling;

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Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses

science (GISci) is the new kid on the block. Its theoretical foundations are therefore still developing and data quality and uncertainty modeling;

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Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling

data concerning the entries and parameters of a system to produce statistical data on the outputs of the system. It is based on papers;

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