Search Results for "em algorithm"
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Showing 8 open source projects for "em algorithm"
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Our computer monitoring software allows employees, field contractors, and freelancers to manually clock in when they begin working on an assignment. The application will take screenshots randomly or at set intervals, which allows employers to observe the work process. The application only tracks activity when the employee is clocked in. No spying, only transparency.
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This means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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imgp
Multi-core image resizer and rotator. Go crunch 'em!
imgp is a command line image resizer and rotator for JPEG and PNG images. If you have tons of images you want to resize adaptively to a screen resolution or rotate by an angle using a single command, imgp is the utility for you. It can save a lot on storage too. Powered by multiprocessing, an intelligent adaptive algorithm, recursive operations, shell completion scripts, EXIF preservation (and more), imgp is a very flexible utility with well-documented easy to use options. imgp intends... -
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rem
REM - Regression models based on expectation maximization algorithm
This project implements regression models based on expectation maximization (EM) algorithms in case of missing data -
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karkinos
Tumor genotyper for Exome sequence that detects SNV,CNV, aTumor purity
karkinos is tumor genotyper which detects single nucleotide variation (SNV), integer copy number variation (CNV) and calculates tumor cellularity from tumor-normal paired sequencing data. Accurate CNV calling is achieved using continuous wavelet analysis and multi-state HMM, while SNV call is adjusted by tumor cellularity and filtered by heuristic filtering algorithm and Fisher Test. Also, Noise calls in low depth region are removed using EM algorithm. -
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MITSU
Stochastic EM for transcription factor binding site motif discovery
MITSU is an algorithm for discovery of transcription factor binding site (TFBS) motifs. It is based on the stochastic EM (sEM) algorithm, which overcomes some of the limitations of deterministic EM-based algorithms for motif discovery. Unlike previous sEM algorithms for motif discovery, MITSU is unconstrained with regard to the distribution of motif occurrences within the input dataset. MITSU also has the ability to automatically determine the most likely motif width by incorporating... -
Define and Deliver Comprehensive Cybersecurity Services. Security threats continue to grow, and your clients are most likely at risk. Small- to medium-sized businesses (SMBs) are targeted by 64% of all cyberattacks, and 62% of them admit lacking in-house expertise to deal with security issues. Now technology solution providers (TSPs) are a prime target. Enter ConnectWise Cybersecurity Management (formerly ConnectWise Fortify) — the advanced cybersecurity solution you need to deliver the managed detection and response protection your clients require. Whether you’re talking to prospects or clients, we provide you with the right insights and data to support your cybersecurity conversation. From client-facing reports to technical guidance, we reduce the noise by guiding you through what’s really needed to demonstrate the value of enhanced strategy.
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sdEM
Stochastic Discriminative Expectation Maximization (sdEM)
Stochastic discriminative EM (sdEM) is an online-EM-type algorithm for discriminative training of probabilistic generative models belonging to the natural exponential family. In this work, we introduce and justify this algorithm as a stochastic natural gradient descent method, i.e. a method which accounts for the information geometry in the parameter space of the statistical model. We show how this learning algorithm can be used to train probabilistic generative models by minimizing different... -
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QuasiRecomb
Probabilistic inference of viral Quasispecies
... parameters by analysing next generation sequencing data. We offer an implementation of the EM algorithm to find maximum a posteriori estimates of the model parameters and a method to estimate the distribution of viral strains in the quasispecies. The model is validated on simulated data, showing the advantage of explicitly taking the recombination process into account, and tested by applying to reads obtained from experimental HIV samples. -
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This little software is the realization of EM algorithm in the application of tossiing the coin, which is described in the paper of Michael Collins in 1997.
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EMPEPA is software that permits to find the most likely rates of a PEPA model according to a set of sample executions by using the EM algorithm. It uses the GNU Scientific Library (GSL).
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