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Multilevel models (also known as hierarchical linear models, linear mixed-effect models, mixed models, nested data models, random coefficient, random-effects models, random parameter models, or split-plot designs) are statistical models of parameters that vary at more than one level. [1]
Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method. [1] The sub-models combine to form the hierarchical model, and Bayes' theorem is used to integrate them with the observed data and account for all the ...
System dynamics and discrete event modeling with some agent-based capabilities. Drag and drop user interface builder allows simulations to be published online. Includes multilevel hierarchical models, reusable modules, multidimensional arrays, optimization, and Monte Carlo analysis. MapleSim: Proprietary, commercial Java (GUI), C, Maple (engine ...
Analytic Hierarchy Process (AHP) Example with Simulations using Matlab – Waqqas Farooq – AHP example for college selection using matlab. An illustrated guide (pdf) – Dr. Oliver Meixner University of Wien – "Analytic Hierarchy Process", a very easy to understand summary of the mathematical theory
The deviance information criterion (DIC) is a hierarchical modeling generalization of the Akaike information criterion (AIC). It is particularly useful in Bayesian model selection problems where the posterior distributions of the models have been obtained by Markov chain Monte Carlo (MCMC) simulation.
Stateflow (developed by MathWorks) is a control logic tool used to model reactive systems via state machines and flow charts within a Simulink model. Stateflow uses a variant of the finite-state machine notation established by David Harel, enabling the representation of hierarchy, parallelism and history within a state chart.
The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of () and requires () memory, which makes it too slow for even medium data sets. . However, for some special cases, optimal efficient agglomerative methods (of complexity ()) are known: SLINK [2] for single-linkage and CLINK [3] for complete-linkage clusteri
Product One-way Two-way MANOVA GLM Mixed model Post-hoc Latin squares; ADaMSoft: Yes Yes No No No No No Alteryx: Yes Yes Yes Yes Yes Analyse-it: Yes Yes No