Search results
Results from the WOW.Com Content Network
Maximum likelihood estimation (MLE) is a standard statistical tool for finding parameter values (e.g. the unmixing matrix ) that provide the best fit of some data (e.g., the extracted signals ) to a given a model (e.g., the assumed joint probability density function (pdf) of source signals).
General numerical computing package with many extension modules. Syntax mostly compatible with MATLAB IGOR Pro: WaveMetrics 1986 1988 8.00 May 22, 2018: $995 (commercial) $225 upgrade, $499 (academic) $175 upgrade, $85 (student) Proprietary: interactive graphics, programmable, 2D/3D, used for science and engineering, large data sets. imc FAMOS
For data requests that fall between the table's samples, an interpolation algorithm can generate reasonable approximations by averaging nearby samples." [8] In data analysis applications, such as image processing, a lookup table (LUT) can be used to transform the input data into a more desirable output format. For example, a grayscale picture ...
Theory-driven evaluation (also theory-based evaluation) is an umbrella term for any approach to program evaluation that develops a theory of change and uses it to design, implement, analyze, and interpret findings from an evaluation. [1] [2] [3] More specifically, an evaluation is theory-driven if it: [4]
In computer network research, network simulation is a technique whereby a software program replicates the behavior of a real network. This is achieved by calculating the interactions between the different network entities such as routers, switches, nodes, access points, links, etc. [1] Most simulators use discrete event simulation in which the modeling of systems in which state variables ...
The CIPP evaluation model is a program evaluation model which was developed by Daniel Stufflebeam and colleagues in the 1960s. CIPP is an acronym for context, input, process and product. CIPP is a decision-focused approach to evaluation and emphasizes the systematic provision of information for program management and operation. [1]
The most common structure learning algorithms assume the data is generated by a Bayesian Network, and so the structure is a directed graphical model. The optimal solution to the filter feature selection problem is the Markov blanket of the target node, and in a Bayesian Network, there is a unique Markov Blanket for each node.
The inspiration for this method of community detection is the optimization of modularity as the algorithm progresses. Modularity is a scale value between −1 (non-modular clustering) and 1 (fully modular clustering) that measures the relative density of edges inside communities with respect to edges outside communities.