Publications by Xinyi Zhu
Xinyi Zhu_ANLY505-2021 late summer.html
Chapter 2 - Small Worlds and Large 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 3 - Sampling the Imaginary-Xinyi Zhu
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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Test#1_Zhu
There are 10 questions and each question (part of a question) is worth 7.5 points each. When completed, knit the file to a .HTML and save the file as Test#1_LastName and submit the .HTML file to the Test #1 assignment link in Canvas. Due Date: Wednesday November 27, 2019 by 11:59p.m. EST. If you have data that is in case form format, and you wan...
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Xinyi Zhu_ANLY505-2021-late summer.html
Chapter 4 - Geocentric Models This chapter (Chapter 4) 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 ...
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505 HW4
Chapter 5 - The Many Variables & the 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 ot...
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505 HW6 Xinyi Zhu
Chapter 7 - Ulysses’ Compass This week 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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505 HW5
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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