Publications by Yili Xu
Assignment 8 - Yili Xu_ANLY505_2022_Late Spring
Chapter 9 - Markov Chain Monte Carlo This chapter has been an informal introduction to Markov chain Monte Carlo (MCMC) estimation. The goal has been to introduce the purpose and approach MCMC algorithms. The major algorithms introduced were the Metropolis, Gibbs sampling, and Hamiltonian Monte Carlo algorithms. Each has its advantages and dis...
2624 sym R (6291 sym/25 pcs) 6 img
Assignment 7_Yili Xu_ANLY505-2022-Late Spring
Chapter 8 - Conditional Manatees This chapter introduced interactions, which allow for the association between a predictor and an outcome to depend upon the value of another predictor. While you can’t see them in a DAG, interactions can be important for making accurate inferences. Interactions can be difficult to interpret, and so the chapt...
2462 sym R (3998 sym/14 pcs) 2 img
Assignment #6_Yili Xu_ANLY505-2022-Late Spring
Chapter 7 - Ulysses’ Compass The chapter began with the problem of overfitting, a universal phenomenon by which models with more parameters fit a sample better, even when the additional parameters are meaningless. Two common tools were introduced to address overfitting: regularizing priors and estimates of out-of-sample accuracy (WAIC and P...
3597 sym R (6431 sym/21 pcs) 1 tbl
Yili Xu_ANLY505-2022-Late Spring
Chapter 6 - The Haunted DAG & The Causal Terror Multiple regression is no oracle, but only a golem. It is logical, but the relationships it describes are conditional associations, not causal influences. Therefore additional information, from outside the model, is needed to make sense of it. This chapter presented introductory examples of some...
3496 sym R (9479 sym/26 pcs) 5 img
Yili Xu_ANLY505-Late-Spring-Assignement #3
Chapter 4 - Geocentric Models This chapter introduced the simple linear regression model, a framework for estimating the association between a predictor variable and an outcome variable. The Gaussian distribution comprises the likelihood in such models, because it counts up the relative numbers of ways different combinations of means and stan...
3002 sym R (3001 sym/11 pcs) 1 img
Yili Xu_ANLY505-2022-Late Spring
Chapter 3 - Sampling the Imaginary This chapter introduced the basic procedures for manipulating posterior distributions. Our fundamental tool is samples of parameter values drawn from the posterior distribution. These samples can be used to produce intervals, point estimates, posterior predictive checks, as well as other kinds of simulations...
2610 sym R (1203 sym/13 pcs) 5 img
Yili Xu_ANLY505-2022-Late Spring
Chapter 5 - Many Variables and Spurious Waffles This chapter introduced multiple regression, a way of constructing descriptive models for how the mean of a measurement is associated with more than one predictor variable. The defining question of multiple regression is: What is the value of knowing each predictor, once we already know the othe...
3255 sym R (6777 sym/43 pcs) 5 img