Three-dimensional representation to show anatomic structures. Models may be used in place of intact animals or organisms for teaching, practice, and study
Three-dimensional representation to show anatomic structures. Models may be used in place of intact animals or organisms for teaching, practice, and study
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc
Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment
Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment
Theoretical representations that simulate the behavior or activity of the cardiovascular system, processes, or phenomena; includes the use of mathematical equations, computers and other electronic equipment
Model Checking -- Kongress -- Zürich <2008> : Objects, components, models and patterns : 46th International Conference, TOOLS Europe 2008, Zurich, Switzerland, June 30-July 4, 2008, proceedings / Richard F. Paige, Bertrand Meyer (eds.)
Theoretical representations that simulate the behavior or activity of chemical processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming
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Model Construction. : A Practical Model-Based Approach to Monetary Policy Analysis-Overview / Douglas Laxton