Method for Estimating Motion Blur Direction and Motion Blur Length Parameters
Implementation of a method for estimating motion blur direction and motion blur length parameters in digital images with code-related algorithm explanations.
Gaussian Mixture Model Implementation with EM Algorithm
This MATLAB implementation solves parameter estimation for Gaussian Mixture Models using the Expectation-Maximization (EM) algorithm. The program modularly separates mean, covariance, and weight estimation into independent functions saved as .M files. The main execution point is through main.m, with sample data provided in spreadsheet format for immediate testing and customization.
Cramér-Rao Bound (CRB)
For parameter estimation problems, the Cramér-Rao Bound (CRB) establishes a lower bound on the variance of any unbiased estimator. This means it's impossible to obtain an unbiased estimator with variance smaller than this bound, providing a benchmark for comparing the performance of unbiased estimators. This program implements the Cramér-Rao Lower Bound (CRLB) with parameterized design, allowing users to customize parameters according to their specific estimation scenarios and requirements.
Comprehensive Copula Function Computation Program
The most complete Copula function computation program to date, featuring parameter estimation algorithms (optimization and simulated annealing), model validation tests (Kolmogorov-Smirnov and Anderson-Darling), and stochastic simulation capabilities.
System Identification and Parameter Estimation
Source code for system identification and parameter estimation, enabling the estimation of unknown models through data-driven algorithms.
Computer Simulation of Various Chemical Engineering Processes
Application Background This code is based on the book "Practical Computer Simulation for Chemical Engineering," which includes MATLAB programming tutorials and calculations for various chemical processes such as distillation columns, reactors, control systems, differential equations, and algebraic equations. The book also provides examples of chemical process optimization. It is recommended to purchase the book and study it alongside this code for effective learning, as the code is specifically written to correspond with the book's examples. Key Technologies The code implements optimization algorithms including quadratic programming and least squares methods. It utilizes MATLAB's Optimization Toolbox to minimize objective functions, covering design optimization, operational optimization, and global optimization. The code also includes parameter estimation and model identification components, such as kinetic parameter estimation and heat transfer parameter calculation.
GARCH(1,1) Maximum Likelihood Estimation with Model Validation
MATLAB implementation for estimating GARCH(1,1) model parameters using Maximum Likelihood Estimation (MLE). Includes simulation-based validation testing for Type I and Type II errors (size and power tests) to ensure correct model specification and statistical reliability.
Parameter Estimation for the GARCH-t Model
Parameter estimation for the GARCH-t model primarily involves utilizing existing stock data to estimate parameters, which includes implementing statistical algorithms and financial modeling techniques in code.
MATLAB Program for AR Model Order Selection and Parameter Estimation
This MATLAB program implements autoregressive (AR) model order determination and parameter estimation for time series analysis and forecasting, featuring comprehensive implementation of key algorithms including Yule-Walker equations and information criteria (AIC/BIC) for optimal model selection.
MATLAB Implementation of Expectation-Maximization (EM) Algorithm
Application Context: In statistical computing, the Expectation-Maximization (EM) algorithm is used to find maximum likelihood or maximum a posteriori estimates of parameters in probabilistic models that depend on unobserved latent variables. The EM algorithm is frequently applied in machine learning and computer vision for data clustering tasks. Key Technology: The EM algorithm iterates through two alternating steps: - E-step (Expectation): Computes the expected value of the log-likelihood function using current estimates of hidden variables - M-step (Maximization): Finds parameters that maximize the expected log-likelihood computed in the E-step Parameters estimated in the M-step are reused in the next E-step, creating an iterative convergence process.