OSCAR Project OSCAR allows users, regardless of their experience level with a *nix environment, to install a Beowulf type high performance computing cluster. It also contains everything needed to administer and program this type of HPC cluster. OSCAR's flexible package management system has a rich set of pre-packaged applications and utilities which means you can get up and running without laboriously installing and configuring complex cluster administration and communication packages. It also lets administrators create customized packages for any kind of distributed application or utility, and to distribute those packages from an online package repository, either on or off site. OSCAR installs on top of a standard installation of a supported Linux distribution. The default OSCAR setup is generally used for scientific computing using a message passing interface (MPI) implementation, several of which are included in the default OSCAR package set. OSCAR on! OSCAR 6.1.1 has been released! (Read more)
Mendel HMM Toolbox for Matlab Written by Steinar Thorvaldsen, 2004. Last updated: Jan. 2006. Dept. of Mathematics and Statistics University of Tromsø - Norway. steinart@math.uit.no MendelHMM is a Hidden Markov Model (HMM) tutorial toolbox for Matlab. To run the program you should make the following steps: 1. When you type "mendelHMM" in Matlab command window the main window of GUI will appear. Main window of the program. In his historic experiment, Gregor Mendel (1822-1884) examined 7 simple traits in the common garden pea (Pisum). Today we know that the recessive expressions most often are mutations in the DNA molecule of the gene, as it is well known for Mendel’s growth gene (trait 7) where a single nucleotide G is substituted with an A. In his experiment Mendel also studied in more detail the plant seeds with two and three heredity factors simultaneously. The estimate of a statistical model according to a training set There are two main types of learning. The sampling of new training data y = (A, A, a, a, a) 1. 2. 3.
PVM: Parallel Virtual Machine PVM (Parallel Virtual Machine) is a software package that permits a heterogeneous collection of Unix and/or Windows computers hooked together by a network to be used as a single large parallel computer. Thus large computational problems can be solved more cost effectively by using the aggregate power and memory of many computers. The software is very portable. PVM enables users to exploit their existing computer hardware to solve much larger problems at minimal additional cost. Current PVM News: A new Russian translation of the site by Andrew Kovalev is available at New German translation of the site is available at The PVM website is now available in Belorussian provided by Fatcow. Current articles from PVM news group comp.parallel.pvm PVM Supported Architectures PVM Documentation: Project Overview A short overview of PVM and its features. HTML Man pages for PVM 3.3.
General Hidden Markov Model Library | Free Science & Engineering software downloads The Julia Language Online Code Repository The goal is to have working code for all the algorithms in the book in a variety of languages. So far, we have Java, Lisp and Python versions of most of the algorithms. There is also some old code in C++, C# and Prolog, but these are not being maintained. Supported Implementations We offer the following three language choices, plus a selection of data that works with all the implementations: Java: aima-java project, by Ravi Mohan. Unsupported Implementations Implementation Choices What languages are instructors recommending? Of course, neither recall nor precision is perfect for these queries, nor is the estimated number of results guaranteed to be accurate, but they offer a rough estimate of popularity.
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