Explore MATLAB source code curated for "参数估计" with clean implementations, documentation, and examples.
1. Master maximum likelihood estimation for multivariate normal distributions through hands-on implementation; 2. Understand Bayesian classification with minimum error rate under multivariate normal distributions; 3. Gain deeper insights into other parameter estimation techniques with practical code examples.
A rapid parameter estimation approach for Generalized Gaussian Distribution with GGD function fitting implementation
Maximum Likelihood Estimation - Achieving Highly Accurate Parameter Estimates with Optimal Speed
Comprehensive methodology for estimating parameters in Markov-Switching (3-state) GARCH models using Markov Chain Monte Carlo techniques
Parameter estimation for the generalized Gaussian model demonstrates excellent performance! This approach effectively handles data modeling with flexible distribution shapes through robust parameter estimation techniques.
This project provides a MATLAB implementation of the graph cut algorithm featuring parameter estimation capabilities applicable to 3D reconstruction. The code demonstrates energy minimization using max-flow/min-cut optimization with adjacency matrix construction.
Implementation of RLS algorithm for adaptive parameter estimation of the system y(n) = 1.5*y(n-1)-0.7*y(n-2)+0.3*u(n-2)
The Linear Frequency Modulated (LFM) signal, as a typical non-stationary signal with large time-bandwidth product, is widely used in various information systems. From electronic warfare and electronic countermeasures perspectives, LFM signals are commonly employed to resolve the contradiction between operating range and range resolution while enhancing signal concealment. The detection and parameter estimation of LFM signals have become key research focuses in electronic warfare. This MATLAB implementation utilizes Fractional Fourier Transform (FRFT) for accurate LFM parameter estimation, featuring complete code examples with operational results demonstrating effective signal processing capabilities.
AR model-based parameter estimation in modern power spectrum analysis provides superior resolution compared to classical spectral estimation methods, offering enhanced frequency component detection through autoregressive coefficient optimization.
A supervised naive Bayes classification algorithm accounting for feature dependencies. Implements parameter estimation, processes training and test datasets, and outputs classification results with accuracy metrics.