Publications by Jiaoyuan Huang
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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 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 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 PSIS)...
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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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In each step, you will process your data for common text data issues. Be sure to complete each one in R and Python separately - creating a clean text version in each language for comparison at the end. Update the saved clean text at each step, do not simply just print it out. Libraries / R Setup In this section, include the libraries you need f...
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For this semester, you have two options: Use your own computer. If you have an older machine, you are likely to have problems with some of the installation of packages. If you have a MacBook Air or other “mini” type machine, you will potentially find you do not have the space for the installed packages. I will help you troubleshoot during t...
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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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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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Objective This project should allow you to apply the information you’ve learned in the course to a new dataset. While the structure of the final project will be more of a research project, you can use this knowledge to appropriately answer questions in all fields, along with the practical skills of writing a report that others can read. The dat...
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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 com...
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