'Coin 1' presentation slideshows

Bayesian Networks (Directed Acyclic Graphical Models)

Coin 1. Coin 2. Bell. Bayesian Networks (Directed Acyclic Graphical Models). The situation of a bell that rings whenever the outcome of two coins are equal can not be well represented by undirected graphical models.

472 views • 33 slides

Bayesian Networks (Directed Acyclic Graphical Models)

Coin 1. Coin 2. Bell. Bayesian Networks (Directed Acyclic Graphical Models). The situation of a bell that rings whenever the outcome of two coins are equal can not be well represented by undirected graphical models.

336 views • 33 slides

Foundations of Sociological Inquiry

433 views • 29 slides

Thursday, 19 April 2007 William H. Hsu Department of Computing and Information Sciences, KSU

Lecture 37 of 42. Unsupervised Learning: AutoClass, SOM, EM, and Hierarchical Mixtures of Experts. Thursday, 19 April 2007 William H. Hsu Department of Computing and Information Sciences, KSU http://www.cis.ksu.edu/Courses/Spring-2007/CIS732 Readings:

425 views • 28 slides

Lecture 4

Lecture 4. Runs. Q: Toss a coin 1000000 times. Run is an unbroken sequence of H in a row What is the length of the longest run Take guesses (closest will get a cookie) We will write an R code togather. Issues. Case sensitive dog and Dog are different Careful of extra spaces Brackets

194 views • 8 slides

256 views • 8 slides