Publications by Pengcheng Zhai
lab2
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. Po...
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Libraries / R Setup In this section, include the R set up for Python to run. ##r chunk library(reticulate) In this section, include import functions to load the packages you will use for Python. ##python chunk import nltk import spacy from spacy import displacy from __future__ import unicode_literals, print_function import plac import rando...
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Be sure to list the group members at the top! This assignment is the first component for your class project. Be sure all group members contribute, and each group member must submit the assignment individually. You can use either R or Python or both for this assignment. You should use the code and packages we used in class assignments, but these c...
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Chapter 2 - Large Worlds and Small Worlds The objectives of this problem set is to work with the conceptual mechanics of Bayesian data analysis. The target of inference in Bayesian inference is a posterior probability distribution. Posterior probabilities state the relative numbers of ways each conjectured cause of the data could have produced th...
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Chapter 11 - God Spiked the Integers This chapter described some of the most common generalized linear models, those used to model counts. It is important to never convert counts to proportions before analysis, because doing so destroys information about sample size. A fundamental difficulty with these models is that parameters are on a different...
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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 disadva...
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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 chapter a...
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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 other pr...
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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. Po...
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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 standard...
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