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). Regularizing priors reduce overfitting during estimation, and WAIC and PSIS help estimate the degree of overfitting. Practical functions compare in the rethinking package were introduced to help analyze collections of models fit to the same data. If you are after causal estimates, then these tools will mislead you. So models must be designed through some other method, not selected on the basis of out-of-sample predictive accuracy. But any causal estimate will still overfit the sample. So you always have to worry about overfitting, measuring it with WAIC/PSIS and reducing it with regularization.
Place each answer inside the code chunk (grey box). The code chunks should contain a text response or a code that completes/answers the question or activity requested. Problems are labeled Easy (E), Medium (M), and Hard(H).
Finally, upon completion, name your final output .html file as: YourName_ANLY505-Year-Semester.html and publish the assignment to your R Pubs account and submit the link to Canvas. Each question is worth 5 points.
7E1. State the three motivating criteria that define information entropy. Try to express each in your own words.
#(1) The uncertainty should be measured on a continuous scale
#(2) The uncertainty should capture the size of the possibility space. Its value is correlated with the number of possible outcomes
#(3) The uncertainty should be additive for independent events 7E2. Suppose a coin is weighted such that, when it is tossed and lands on a table, it comes up heads 70% of the time. What is the entropy of this coin?
p <- c(0.7, 1 - 0.7)
H <- -sum(p * log(p))
H## [1] 0.61086437E3. Suppose a four-sided die is loaded such that, when tossed onto a table, it shows “1” 20%, “2” 25%, “3” 25%, and “4” 30% of the time. What is the entropy of this die?
p <- c(0.20, 0.25, 0.25, 0.30)
H <- -sum(p * log(p))
H## [1] 1.3762277E4. Suppose another four-sided die is loaded such that it never shows “4”. The other three sides show equally often. What is the entropy of this die?
p <- c(1/3, 1/3, 1/3)
H <- -sum(p * log(p))
H## [1] 1.0986127M1. Write down and compare the definitions of AIC and WAIC. Which of these criteria is most general? Which assumptions are required to transform the more general criterion into a less general one?
# AIC is an estimator of out-of-sample prediction error and estimates the quality of each model. AIC is a way to select model quality and evaluate overfitting in the model.
# WAIC is the generalized version of AIC onto singular statistical models.
# WAIC is the most general criteria
# To transform from WAIC to AIC, we need to assume that the posterior distribution is approximately multivariate Gaussian and the priors are flat or overwhelmed by the likelihood.7M2. Explain the difference between model selection and model comparison. What information is lost under model selection?
#Model selection is to select the model with the lowest information criterion value and to discard all other models with higher values. This practice loses information about relative model accuracy. Model averaging is using Bayesian information criteria to construct a posterior predictive distribution and leverages the uncertainty in multiple models. This practice does not lose information on its own.7M3. When comparing models with an information criterion, why must all models be fit to exactly the same observations? What would happen to the information criterion values, if the models were fit to different numbers of observations? Perform some experiments, if you are not sure.
# Information criteria are based on deviance, which is accrued over observations without being divided by the number of observations. So a model with more observations will have a higher deviance and thus worse accuracy according to information criteria. It is unaccurate to contrast models fit to different numbers of observations.7M4. What happens to the effective number of parameters, as measured by PSIS or WAIC, as a prior becomes more concentrated? Why? Perform some experiments, if you are not sure.
# With more concentrated priors, the likelihood will become more concentrated as well and thus variance will decrease.As a result, the effective number of parameters decreases.7M5. Provide an informal explanation of why informative priors reduce overfitting.
# Informative priors restrains the range of parameters so that It will be less extreme data/outliers to overfit the model. 7M6. Provide an informal explanation of why overly informative priors result in underfitting.
# Overly informative priors provide too narrow range of parameters so that there will be less valuable data/parameters and too simple to build a model.