A python module for the simulation of aroma transport processes during conching of dark chocolate
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Updated
Jun 13, 2022 - Python
A python module for the simulation of aroma transport processes during conching of dark chocolate
Uncertain parameter estimation on Grey-Box Dynamic Systems
Documentation of the Generalized Weibull Regression REU Project completed at the University of Michigan Dearborn Summer 2023
OLS. R and Python. In this project, we study fundamental concepts of Supervised ML models, such as Regression Analysis: Coefficient of Model Adjustment (R²), Parameters Estimation ,Statistical Significance of the Model (F test, T test) ,Multiple Regression , Qualitative Explanatory Variables (X) , heteroscedasticity and etc.
Projects for Systems Modeling & Simulation Course / Aristotle University of Thessaloniki / Summer Semester 2021
Reduced Dimension Ensemble Modeling and Parameter Estimation
A very simple SIR model implementation and Levenberg-Marquardt fit with the Johns Hopkins CSSE data for COVID-19.
From A to Z
Simulate a Induction Motor and know its characteristics like as the torque, current, power, PF for different slips(speeds), all this using the machine parameters or the test data.
This repository contains comprehensive information on the steps necessary for parameter selection and statistical tests and model selection
Analyze the performance of thousands of students with just one sample. Determine the socioeconomic variables that increase a student's performance and graphically visualize the results
Assignments completed for my Machine Learning course: Topics include probability and statistics proofs, MLE/MAP parameter estimation, EM Algorithm, Bayes Theorem implementations, gradient descent methods, Neural Networks and Deep Learning.
Parameter Estimation is a branch of statistics that involves using sample data to estimate the parameters of a distribution.
Small collection of numerical experiments
identPy GUI application, a software for parameter estimation of nonlinear dynamic systems.
Imoto, H., Zhang, S. & Okada, M. A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway. Cancers (Basel). 12, 2878 (2020).
Code for parameter estimation in interacting particle systems.
Statistical framework to perform parameter estimation with normalizing flows
this repository is for the cosmology course in the Winter of 1401. you can find out your computational homework here.
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