Search Results for "em algorithm"
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Showing 13 open source projects for "em algorithm"
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PRML
PRML algorithms implemented in Python
PRML repository is a respected and well-maintained project that implements the foundational algorithms from the famous textbook Pattern Recognition and Machine Learning by Christopher M. Bishop, providing a practical and accessible Python reference for both students and professionals. Rather than just summarizing concepts, the repository includes working code that demonstrates linear regression and classification, kernel methods, neural networks, graphical models, mixture models with EM... -
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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
...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. -
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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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. -
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SIMBUCA (before called Simonion) is a simulation package that simulates the motion of charged particles under the influence of Electric and/or Magnetic fields. What makes Simbuca unique is that you can choose to calculate the Coulomb interaction between ions on a Graphics cards which is much faster than calculating this on the conventional CPU (reducing the simulation time from years to days). Therefore Simbuca has been applied in various projects which required to understand the...
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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. -
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ClusterMX
The ClusterMX program implements various clustering algorithms
The ClusterMX program implements various clustering algorithms including 1) K-Means clustering optimized by random walks; 2) Weighted K-Means (applying force filed to the multidimensional clustering space); 3) EM Clustering Algorithm; 4) Multi-Model Mean Shift Clustering with Random Sampling; 5) Unsupervised K-Wishart clustering. -
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QuasiRecomb
Probabilistic inference of viral Quasispecies
...We present a jumping hidden Markov model that describes the generation of the viral quasispecies and a method to infer its 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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GSP program are based on Bayesian framework with an EM algorithm to predict genome size iteratively, which is elegant in mathematics. The model first develop under the no sequencing error model, then extend to the sequencing errors containing model.
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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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em
Expectation Maximization (EM) algorithms
The project implements Expectation Maximization (EM) algorithm together with its variants
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