maximum-likelihood-estimation

There are 203 repositories under maximum-likelihood-estimation topic.

  • MatthewReid854/reliability

    Reliability engineering toolkit for Python - https://reliability.readthedocs.io/en/latest/

    Language:Python390224585

  • dkanungo/Probabilistic-ML-for-finance-and-investing

    Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python

    Language:Jupyter Notebook1095046
  • models-by-example

    m-clark/models-by-example

    By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.

    Language:R712019

  • Blue-Universe/Time-Series-Analysis-Statistical-Arbitrage

    This project used GARCH type models to estimate volatility and used delta hedging method to make a profit.

    Language:Jupyter Notebook663015

  • physhik/Study-of-David-Mackay-s-book-

    David Mackay's book review and problem solvings and own python codes, mathematica files

    Language:Jupyter Notebook574010

  • jkirkby3/pymle

    Maximum Likelihood estimation and Simulation for Stochastic Differential Equations (Diffusions)

    Language:Python541413

  • odubno/gauss-naive-bayes

    Gauss Naive Bayes in Python From Scratch.

    Language:Python452021

  • monty-se/PINstimation

    A comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.

    Language:R40147

  • gbroques/naive-bayes

    A Python implementation of Naive Bayes from scratch.

    Language:Python393027

  • ecrc/exageostat

    A High Performance Unified Framework for Geostatistics on Manycore Systems.

    Language:C385016

  • viodotcom/ppca_rs

    Python+Rust implementation of the Probabilistic Principal Component Analysis model

    Language:Rust36422

  • ankitbit/Advanced_Statistical_Inference

    This repository has scripts and other files that are part of the lecture notes and assignments of the course "Advanced Statistical Inference" taught at FME, UPC Barcelonatech.

    Language:HTML253012

  • gcampanella/pydata-london-2018

    Slides and notebooks for my tutorial at PyData London 2018

    Language:Jupyter Notebook21306

  • stat-ml/GeoMLE

    This repo contains code for GeoMLE intrinsic dimension estimation algorithm

    Language:Jupyter Notebook20543

  • KeplerGO/oktopus

    🐙: Maximum likelihood model estimation using scipy.optimize

    Language:Python19455

  • AliAmini93/Data-Distribution-Finder

    Developed a Windows-based app for analyzing data distributions and identifying the best-fitted distribution using the Maximum Likelihood Estimation algorithm. The app features histogram analysis, error ranking, and allows users to directly save results along with distribution charts.

    Language:Python1810

  • polyactis/Accucopy

    Accucopy is a computational method that infers Allele-Specific Copy Number alterations from low-coverage low-purity tumor sequencing data.

    Language:C++172234

  • jungm2018/communications_neural_net

    Implementation of Neural Nets for Communications Channel Decoding using Log Likelihood Ratios

    Language:Jupyter Notebook16102

  • rsnemmen/Fermi-LAT-tutorial

    Lectures on maximum likelihood estimation for astroparticle physics

    Language:Jupyter Notebook16305

  • cescalara/icecube_tools

    Python tools for working with the IceCube public data.

    Language:Python15398

  • jkirkby3/BsplineDensity

    B-Spline Density Estimation Library - nonparametric density estimation using B-Spline density estimator from univariate sample.

    Language:MATLAB15103

  • oliviergimenez/multievent_jags_R

    Fit multievent capture-recapture models in R (maximum-likelihood), Nimble and JAGS (Bayesian)

    Language:R14101

  • vanTeeffelenLab/ExTrack

    ExTrack MLE for diffusive noisy single-particle tracks

    Language:Jupyter Notebook14314

  • oguzhan-baser/Channel-Estimation

    Estimating unknown static channel coefficients on a communication system utilizing Maximum Likelihood Single-Shot Estimation algorithm.

    Language:MATLAB12104
  • ideological-embeddings

    corradomonti/ideological-embeddings

    Code and data for the CIKM2021 paper "Learning Ideological Embeddings From Information Cascades"

    Language:Python11103

  • upathare1/Advanced-Term-Structures

    Our project extends the classical models such as Vasicek and CIR to incorporate the effects of jump-risks in the market. We explore modern methods to price and calibrate such models and evaluate their pricing performance with respect to classical models and the observed market prices.

    Language:Jupyter Notebook11304

  • hz-zhu/NPD-micro-saccade-detection

    Neyman–Pearson Detector (NPD) for saccadic eye movements

    Language:C++8002

  • magister-informatica-uach/INFO337

    Herramientas estadĂ­sticas para la investigaciĂłn

    Language:Jupyter Notebook8007

  • timothee-bacri/HMM_with_TMB

    A gentle tutorial of accelerated parameter and confidence interval estimation for Hidden Markov Models using Template Model Builder

    Language:R8201

  • jiaxiang-cheng/Random-Weighted-Bootstrap-with-Weibull

    Reproduction of the work by Hong, Y., Meeker, W. Q., & McCalley, J. D. (2009). Prediction of remaining life of power transformers based on left truncated and right censored lifetime data. Annals of Applied Statistics, 3(2), 857-879.

    Language:R7101

  • romain-e-lacoste/sparklen

    A statistical learning toolkit for high-dimensional Hawkes processes in Python

    Language:Python710

  • WilliamBidle/QST

    A public Python package to perform quantum state tomography through maximum likelihood estmation

    Language:Python7201

  • raphael-group/LAML

    LAML is a maximum likelihood algorithm to infer cell phylogeny from dynamic lineage tracing data

    Language:Python6811

  • kyomangold/ETH-MachineLearning

    Repository for the code of the "Introduction to Machine Learning" (IML) lecture at the "Learning & Adaptive Systems Group" at ETH Zurich.

    Language:Python5100

  • pandafengye/MIST

    MIST: a metagenomic intra-species typing tool.

    Language:Python5000

  • prakHr/NeuralNetworksAndFuzzyLogic

    [College Course] - Course: BITS F312 Neural Network and Fuzzy Logic

    Language:Jupyter Notebook5102