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py_gmm G. Pellegrini [20] Python + Fortran A Python + Fortran 90 implementation of the Generalized Multiparticle Mie method, especially suited for plasmonics and near field computation. 2017 CELES A. Egel, L. Pattelli and G. Mazzamuto [21] MATLAB + CUDA Running on NVIDIA GPUs, with high performance for many spheres. 2020 QPMS M. Nečada [22] C ...
Here F N is the Newtonian force, m is the object's (gravitational) mass, a is its acceleration, μ (x) is an as-yet unspecified function (called the interpolating function), and a 0 is a new fundamental constant which marks the transition between the Newtonian and deep-MOND regimes. Agreement with Newtonian mechanics requires
The EM algorithm consists of two steps: the E-step and the M-step. Firstly, the model parameters and the () can be randomly initialized. In the E-step, the algorithm tries to guess the value of () based on the parameters, while in the M-step, the algorithm updates the value of the model parameters based on the guess of () of the E-step.
In the spherical-coordinates example above, there are no cross-terms; the only nonzero metric tensor components are g rr = 1, g θθ = r 2 and g φφ = r 2 sin 2 θ. In his special theory of relativity, Albert Einstein showed that the distance ds between two spatial points is not constant, but depends on the motion of the observer.
There are many useful features of the effective potential, such as . To find the radius of a circular orbit, simply minimize the effective potential with respect to , or equivalently set the net force to zero and then solve for : = After solving for , plug this back into to find the maximum value of the effective potential .
For two pairwise interacting point particles, the gravitational potential energy is the work that an outside agent must do in order to quasi-statically bring the masses together (which is therefore, exactly opposite the work done by the gravitational field on the masses): = = where is the displacement vector of the mass, is gravitational force acting on it and denotes scalar product.
In econometrics and statistics, the generalized method of moments (GMM) is a generic method for estimating parameters in statistical models.Usually it is applied in the context of semiparametric models, where the parameter of interest is finite-dimensional, whereas the full shape of the data's distribution function may not be known, and therefore maximum likelihood estimation is not applicable.
C code for the Earth Mover's Distance (archived here) Python implementation with references; Python2 wrapper for the C implementation of the Earth Mover's Distance; C++ and Matlab and Java wrappers code for the Earth Mover's Distance, especially efficient for thresholded ground distances