Search Results for "probability" - Page 3
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Showing 160 open source projects for "probability"
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SMTracker (v1.5, v2.0)
A tool for analysis and visualization of single-molecule tracking data
SMTracker v2.0 is a MATLAB-based graphical user interface (GUI) for automatically quantifying, visualising and managing SMT data via five interactive panels, allowing the user to interactively explore tracking data from several conditions, movies and cells on a track-by- track basis. Diffusion parameters and motion behaviour is analysed by several methods: a) by a Gaussian mixture model ,or b) by using the cumulative probability distribution of square displacements, c) Mean-Squared displacement fits, d) by Jump Distance analysis. It also includes exploratory tools to visualise single trajectories or dynamic heat maps. -
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LaPath
Learning Automata algorithm for the shortest path problem.
...In the context of network routing, LA residing at intermediate nodes along a path, exploit feedback from the destination node for reducing, e.g., path's length. According to topology’s resources like the node and edge numbers, the proper number of iterations must be used. More iterations lead to paths with higher probability of being optimal but more computing resources are consumed. Development takes place at https://github.com/zfoxer/LaPath -
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Kalman and Bayesian Filters in Python
Kalman Filter book using Jupyter Notebook
...All code is written in Python, and the book itself is written using Juptyer Notebook so that you can run and modify the code in your browser. What better way to learn? This book teaches you how to solve all sorts of filtering problems. Use many different algorithms, all based on Bayesian probability. In simple terms Bayesian probability determines what is likely to be true based on past information. This book is interactive. While you can read it online as static content, it's better to use it as intended. It is written using Jupyter Notebook, which allows you to combine text, math, Python, and Python output in one place. -
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Mathematics
Accumulation of mathematical knowledge, matrix numerical optimization
Mathematics is a comprehensive collection of notes, resources, and references spanning a wide range of mathematical topics. The repository organizes material across pure and applied mathematics, including calculus, linear algebra, probability, statistics, and optimization. It also extends into computational and algorithmic applications of mathematics, making it a useful reference for both academic study and practical problem-solving. The goal is to provide learners, researchers, and developers with a consolidated source of foundational and advanced mathematical concepts. ... -
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jags-zibg
Zero-inflated bivariate geometric distributions in JAGS
The JAGS ZIBGeometric module extends JAGS by providing a zero-inflated bivariate geometric distribution class. Currently, it only supports probability mass computations in posterior parameter distribution estimation. -
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Machine Learning cheatsheets Stanford
VIP cheatsheets for Stanford's CS 229 Machine Learning
...The repository includes summaries covering areas such as supervised learning, unsupervised learning, deep learning, and optimization techniques. In addition to machine learning algorithms, it also contains refresher materials on mathematical prerequisites including probability theory, statistics, linear algebra, and calculus. These cheat sheets are designed to serve as quick reference guides that students can use while studying or reviewing machine learning material. -
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Think Bayes
Code repository for Think Bayes
...Instead of heavy focus on continuous mathematics or calculus, the book emphasizes learning Bayesian inference by writing Python programs. The project includes code examples, scripts, and environments that correspond to the chapters of the book. Learners can run the code, experiment with probability distributions, compute posterior probabilities, and understand Bayesian updating via simulation and algorithmic methods. The book and code encourage thinking in terms of discrete approximations (sums over distributions) rather than continuous integrals, making it more accessible to many programmers. Over time, the repository has been updated (including a second edition version) to reflect improved practices, corrections, and modern Python tooling. -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
...The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. At the same time, it also introduces deep learning techniques used by practitioners in the industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling and practical methods, and investigates topics such as natural language processing, Applications in speech recognition, computer vision, online recommender systems, bioinformatics, and video games. ... -
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smib is one of the smallest and simplest computer algebra system in the world, but simple does not mean simplistic. Using smib you can explore many branches of mathematics (e.g. number theory, algebra, calculus, numerical analysis, differential geometry, probability and stochastic calculus) and also some physics (e.g. electromagnetism and quantum mechanics).. By simple you can understand easy to program (smib is a dynamically typed language, with strong affinity to recursivity, programs are often short and powerful and maybe useful for teaching), and also easy to modify (source code is free, written in C language, and based on notions of stacks and lists). ...
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Newsvendor Model Simulation Spreadsheet
Excel Spreadsheet Model for Single Period Inventory Problems
The spreadsheet (Excel) of a single-period inventory model with stochastic demand can be used as a simulation tool for engineering education or Decision Support System. Based on spreadsheet techniques and examples described in the following sources: Albright S. C., & Winston W. L. (2005). Spreadsheet modeling and applications: essentials of practical management science, South-Western Pub. Albright, S. C. W. C., Winston, W., & Zappe, C. (2010). Data analysis and decision making. Cengage... -
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OpenPPM
Project Portfolio Management
Open PPM is an open source product, used for Project Management and Project Portfolio Management, in accordance with the PMBOK® (Project Management Body of Knowledge) guide, from PMI® (Project Management Institute). -
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jieba
Stuttering Chinese word segmentation
"Jaba" Chinese word segmentation, do the best Python Chinese word segmentation component. Four word segmentation modes are supported. Precise mode, which tries to cut the sentence most precisely, suitable for text analysis. Full mode, scans all the words that can be formed into words in the sentence, the speed is very fast, but the ambiguity cannot be resolved. The search engine mode, on the basis of the precise mode, divides the long words again to improve the recall rate, which is suitable... -
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PRMLT
Matlab code of machine learning algorithms in book PRML
...Many tricks for speeding up Matlab code are applied (e.g. vectorization, matrix factorization, etc.). Usually, functions in this package are orders faster than Matlab builtin ones (e.g. kmeans). Many tricks for numerical stability are applied, such as computing probability in logrithm domain, square root matrix update to enforce matrix symmetry. -
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Probability Cheatsheet
A comprehensive 10-page probability cheatsheet
The probability_cheatsheet is a cheat sheet repository that summarizes key probability theory concepts, formulas, distributions, and properties in a concise format. It likely includes definitions of random variables, PMFs and PDFs, expectations, variance, common distributions (e.g. binomial, normal, Poisson, exponential), conditional probability, Bayes’ theorem, moment generating functions, and perhaps important inequalities (Markov, Chebyshev, Chernoff). -
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MimiPenguin
A tool to dump the login password from the current linux user
...Adapted from the idea behind the popular Windows tool mimikatz. Takes advantage of cleartext credentials in memory by dumping the process and extracting lines that have a high probability of containing passwords. Will attempt to calculate each word's probability by checking hashes in memory, and regex searches. 2.0 introduces a clean port that aims to increase the speed of execution and portability -
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GSMLBook
Recipes for basic machine learning algorithms using sklearn in jupyter
...Topics include linear, multilinear, polynomial, stepwise, lasso, ridge, and logistic regression; ROC curves and measures of binary classification; nonlinear regression (including an introduction to gradient descent); classification and regression trees; random forests; neural networks; probabilistic methods (KNN, naive Bayes', QDA, LDA); dimensionality reduction with PCA; support vector machines; and clustering with K-Means, hierarchical, and DBScan. Appendices provide a review of probability and linear algebra. While some mathematical foundation is provided, it is not essential for understanding the implementations. The target audience is advanced community college and university students. -
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PhunkBox
Enjoy your music.
Funkbox is a music player with auto rating and probability-based randomizer. The project is currently functional, however development in the near future may invalidate user library, resetting the learned statistics. -
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Objective Function Analysis
An alternative to neural nets for machine learning.
Objective Function Analysis models knowledge as a multi-dimensional probability density function (MD-PDF) of the perceptions and responses (which are themselves perceptions) of an entity and an objective function (OF). The learning algorithm is the action of choosing a response, given the perceptions, which maximizes the objective function. The MD-PDF is initially seeded by a uniform random number generator. -
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Awesome Math
This is the Curriculum for "How to Learn Mathematics Fast"
This repository is a curated roadmap for learning the core mathematics used in computer science, machine learning, and data science without getting lost in unnecessary detours. It organizes topics like algebra, calculus, linear algebra, probability, and statistics into a pragmatic sequence that favors intuition and problem-solving over purely formal proofs. The materials emphasize short, high-leverage resources—video lectures, concise notes, and hands-on exercises—that help you build momentum quickly. It also suggests checkpoints and practice ideas so you can test comprehension and move forward with confidence. ... -
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Reliability Sensitivity Analysis
Reliability sensitivity analysis in a single spreadsheet using VBA
1- Define random variables using mean and standard deviation 2- Define limit state function 3- Get the sensitivity derivatives with respect to all random variables -
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ProbAbilistic CALculator - a package for computing with probability distributions
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SCRAM
Probabilistic Risk Assessment Tool
SCRAM is a free and open source probabilistic risk analysis tool. The tool is under development to include fault tree, event tree, common cause, and other standard analyses. -
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SCEO
Single Cell Event Ordering Algorithm
...Usage: java -jar SCEO.jar input=input.txt output=output.txt N=n with input = input file containing tab delimited matrix with for each gene a (named) row and for each cell a (named) column with the values 1=event present, 0=event absent, -1=NA output = output file with an enumeration of the most possible orders of events, preceded by a value indicating the probability of this option. N = number of paths to be in the output -
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auto_ml
Automated machine learning for analytics & production
auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. Before you go any further, try running the code. Load up some data (either a DataFrame, or a list of dictionaries, where each dictionary is a row of data). Make a column_descriptions dictionary that tells us which attribute name in each row represents the value we’re... -
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Seq2seq Chatbot for Keras
This repository contains a new generative model of chatbot
...This trained model can be fine-tuned using a closed-domain dataset to real-world applications. The canonical seq2seq model became popular in neural machine translation, a task that has different prior probability distributions for the words belonging to the input and output sequences since the input and output utterances are written in different languages. The architecture presented here assumes the same prior distributions for input and output words. Therefore, it shares an embedding layer (Glove pre-trained word embedding) between the encoding and decoding processes through the adoption of a new model.
