Search Results for "numerical"
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Showing 7 open source projects for "numerical"
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TU Calc
Simple tool for numerical calculation
TU Calc is a modular tool that aims to gather numerical methods for solution of mathematical problems. Interacting with the user through a GUI that is analogous to the windows desktop. -
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Tail Probability Calculator
Tail probability calculator for continuous random variable
A suite of Matlab functions that calculate the tail probability / cdf / pdf / quantile of linear combination of random variables in one of the following classes: (1) symmetric random variables with support on the real axis (normal, Student's t, uniform and triangular); (2) random variables with support on the positive real axis (chi-squared and log-Lambert W x chi-squared distributions; inverse gamma distribution is temporarily disabled due to numerical issues). -
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IterInt is a numerical integration package that implements iterated numerical integration methods. Iterated methods can be used to compute low dimensional (less than ten, for example) integration problems to get very accurate results.
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Math Simulator
A simulation software for numerical techniques.
Math Simulator is a java-based application which simulates various mathematical techniques.It is extremely useful for the Engineering students and for professionals. Most importantly, unlike most others Math Simulator is an Application rather than a java - library. So, user is not required to have any computer background. A Unique feature of Math Simulator is that it allows you to save queries for later reference. Hence you can save the problems along with their results. -
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A Matlab toolbox for interfacing with the pure JAVA numerical library Snifflib. This toolbox provides convenience m-files for interoperability with Snifflib from within an active Matlab session running a JAVA virtual machine.
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Math.NET aims to provide a self contained clean framework for symbolic mathematical (Computer Algebra System) and numerical/scientific computations, including a parser and support for linear algebra, complex differential analysis, system solving and more
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...The considered implemented parametric and non-parametric Monte Carlo likelihood methods were chosen for their ability to treat both one-dimensional and multivariate SDE systems, although the quality of the inferential results can't match those obtained using more advanced techniques. Nevertheless the toolbox capabilities to simulate numerical solutions of SDE systems are still valid and can serve as a useful starting point to those willing to simulate stochastic dynamical models easily.
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