- Learning Probabilistic Graphical Models in R
- David Bellot
- 118字
- 2025-04-04 20:03:16
Conventions
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We can also mention the arm
package, which provides Bayesian versions of glm()
and polr()
and implements hierarchical models."
Any command-line input or output is written as follows:
pred_sigma <- sqrt(sigma^2 + apply((T%*%posterior_sigma)*T, MARGIN=1, FUN=sum)) upper_bound <- T%*%posterior_beta + qnorm(0.95)*pred_sigma lower_bound <- T%*%posterior_beta - qnorm(0.95)*pred_sigma