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PLUMED is an open-source library implementing enhanced-sampling algorithms, various free-energy methods, and analysis tools for molecular dynamics simulations. It is designed to be used together with ACEMD, AMBER, DL_POLY, GROMACS, LAMMPS, NAMD, OpenMM, ABIN, CP2K, i-PI, PINY-MD, and Quantum ESPRESSO, but it can also be used together with analysis and visualization tools VMD, HTMD, and ...
The paper was accompanied by a software package written in TensorFlow release on GitHub. [10] It was reimplemented in PyTorch by lucidrains. [11] [12] On December 20, 2021, the LDM paper was published on arXiv, [13] and both Stable Diffusion [14] and LDM [15] repositories were published on GitHub. However, they remained roughly the same.
A product distribution is a probability distribution constructed as the distribution of the product of random variables having two other known distributions. Given two statistically independent random variables X and Y, the distribution of the random variable Z that is formed as the product = is a product distribution.
SymPy is simple to install and to inspect because it is written entirely in Python with few dependencies. [4] [5] [6] This ease of access combined with a simple and extensible code base in a well known language make SymPy a computer algebra system with a relatively low barrier to entry.
Biskit is an open source software package that facilitates research in structural bioinformatics and molecular modelling.Written in Python, it consists of: . An object-oriented programming library for manipulating and analyzing macromolecular structures, protein complexes and molecular dynamics trajectories
The squared Mahalanobis distance () is decomposed into a sum of k terms, each term being a product of three meaningful components. [6] Note that in the case when k = 1 {\displaystyle k=1} , the distribution reduces to a univariate normal distribution and the Mahalanobis distance reduces to the absolute value of the standard score .
The characteristic function + = ((+)) of the sum of two independent random variables X and Y is just the product of the two separate characteristic functions: = (), = ()
The product of two Gaussian functions is a Gaussian, and the convolution of two Gaussian functions is also a Gaussian, with variance being the sum of the original variances: = +. The product of two Gaussian probability density functions (PDFs), though, is not in general a Gaussian PDF.