This repository provides implementations for a better understanding of PRML(Pattern Recognition and Machine Learning).
- Implementation of Noise Reduction Using Graphical Model
- Implementing Gaussian Process Regression
- Fitting Trigonometric Functions Using the Nadaraya-Watson Model
- Implementing Mixture Density Network(MDN) Using Pytorch
- Understanding the Tanh Function as an Activation Function
- Implementation of Stochastic Generative Model
- Implementing Fisher’s Linear Discriminant
- Hyperparameter Estimation Using Evidence Approximation
- Plotting Equivalent Kernel
- Plotting Predicted Distribution of Bayesian Linear Regression Model
- Plotting the Distribution of Parameters in Bayesian Linear Regression
- Plotting Relationship between Bias and Variance
- Plotting Basis Functions
- Plotting Polynomial Curve Fitting
- Plotting Mixture of Gaussians
- Plotting von Mises Distribution
- Plotting Student’s t-distribusion
- Bayesian Inference for the Mean of a Gaussian Distribution with Known Mean
- Bayesian Inference for the Mean of a Gaussian Distribution with Known Variance
- Plotting Conditional Gaussian Distribution
- Plotting Gaussian Distribution
- Plotting Probability Distributions for Binary and Multivalued Variables
