# WeiYa's Work Yard

## First Glance at KEGGgraph

##### November 21, 2022

This post is based on

## scDesign3: A Single-cell Simulator

##### October 09, 2022

This post is based on

## Contrastive Learning: A Simple Framework and A Theoretical Analysis

##### October 06, 2022

This note is based on

## Debiased Inverse-Variance Weighted Estimator in Mendelian Randomization

##### September 20, 2022

This post is for the talk at Yale given by Prof. Ting Ye based on the paper Ye, T., Shao, J., & Kang, H. (2020). Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization (arXiv:1911.09802). arXiv.

## Monotone Multi-Layer Perceptron

##### July 04, 2022

This note is for monotonic Multi-Layer Perceptron Neural network, and the references are from the R package monmlp.

## Scale Mixture Models

##### March 25, 2022

This note is for scale mixture models.

## Equivariance

##### March 22, 2022

This post is for Chapter 3 of Lehmann, E. L., & Casella, G. (1998). Theory of point estimation (2nd ed). Springer.

## Applications with Scale Parameters

##### March 22, 2022

This note contains several papers related to scale parameter.

## Empirical Bayes

##### January 16, 2022

This note is based on Sec. 4.6 of Lehmann, E. L., & Casella, G. (1998). Theory of point estimation (2nd ed). Springer.

## Generalizing Ridge Regression

##### December 14, 2021

This note is for Chapter 3 of van Wieringen, W. N. (2021). Lecture notes on ridge regression. ArXiv:1509.09169 [Stat].

## Gaussian Processes for Regression

##### December 13, 2021

This note is for Chapter 4 of Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.

## Probabilistic Principal Curves

##### November 22, 2021

This note is for Chang, K.-Y., & Ghosh, J. (2001). A unified model for probabilistic principal surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(1), 22–41., but only involves the principal curves.