This shows you the differences between two versions of the page.

Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||

cs-401r:assignment-2 [2014/09/12 08:27] ringger |
cs-401r:assignment-2 [2014/09/12 08:52] ringger |
||
---|---|---|---|

Line 1: | Line 1: | ||

- | = Bayes Nets = | + | = Probability Theory = |

== Objectives == | == Objectives == | ||

Line 11: | Line 11: | ||

Show your work. Be clear and concise. '''This assignment must be typed.''' | Show your work. Be clear and concise. '''This assignment must be typed.''' | ||

- | |||

- | === Bayes Net === | ||

- | |||

- | [[http://nlp.cs.byu.edu/~plf1/cs401rpml/GraphicalModel.png|Model (open in a new tab)]] | ||

# [10 points] Prove that the relationship we call ''conditional independence'' is symmetric. In other words, Prove either (a) or (b) (since they are equivalent), and apply the same [[Proofs|standard of proof]] as in assignment 1: | # [10 points] Prove that the relationship we call ''conditional independence'' is symmetric. In other words, Prove either (a) or (b) (since they are equivalent), and apply the same [[Proofs|standard of proof]] as in assignment 1: | ||

Line 20: | Line 16: | ||

#* (b) $P(X, Y | Z) = P(X | Z) \cdot P(Y|Z)$ if and only if $P(Y, X | Z) = P(Y | Z) \cdot P(X | Z)$ | #* (b) $P(X, Y | Z) = P(X | Z) \cdot P(Y|Z)$ if and only if $P(Y, X | Z) = P(Y | Z) \cdot P(X | Z)$ | ||

#** (in other words, the "given $Z$" stays the same, while $X$ and $Y$ trade places). | #** (in other words, the "given $Z$" stays the same, while $X$ and $Y$ trade places). | ||

- | Consider the graphical model (shown above) over five binary random variables: | + | # [20 points: 10 points each] (based on exercise 2.2 in Koller and Friedman) Independence: |

- | # [10 points] Factor the joint distribution represented by the entire model shown in the figure according to the explicit independence assumptions represented in the model. | + | #* Prove that for binary random variables $X$ and $Y$, the event-level independence $(x^0 \bot y^0)$ implies random-variable independence $(X \bot Y)$. Use the usual standard of proof. |

- | # [20 points] List the independence assumptions captured in the model (in the above figure) between the following pairs of random variables. Be sure to consider all of the cases in which the other variables in the model have known values and when they do not. | + | #* Give a counterexample for nonbinary variables. |

- | #* $L$ and $R$ | + | # [20 points] Consider how to sample from a categorical distribution over four colors. Think of a spinner with four regions having probabilities $p_{red}$, $p_{green}$, $p_{yellow}$, and $p_{blue}$. Write pseudo-code for choosing a sample from this distribution. |

- | #* $T$ and $R$ | + | # [10 points] Does your pseudo-code scale to a distribution over ten thousand values? If not, rewrite it. If so, say why. |

- | # [20 points] More independence: | + | # [20 points] Implement your pseudo-code, choose values for the four probabilities on the spinner as parameters to your procedure, and run it 100 times. Give the results as a vector of counts over the four colors. |

- | #* Assuming that the value of $L$ is known, list ''all'' independence relations between $T$ and other variables. Be sure to consider all of the cases in which the other variables in the model have known values and when they do not. | + | # [10 points] Normalize your count vector by 100. How does the result compare with your chosen parameters? |

- | #* Assuming that the value of $S$ is known, list ''all'' independence relations between $T$ and other variables. Be sure to consider all of the cases in which the other variables in the model have known values and when they do not. | + | # [10 points] Compute the mean and variance of the estimated multinomial distribution you just discovered. |

- | # [10 points] Write the four entries and their values in the conditional distribution for $P(L=0 | M=m,S=s)$ (for $m \in \{0,1\}$ and $s \in \{0,1\}$). | + | |

- | # [10 points] (a) Write an expression for the joint probability $P(T=1, R=0, L=0, M=0, S=1)$ in terms of the probabilities given in the model (use the symbolic forms). (b) Then compute the actual probability. | + | |

- | # [10 points] Compute $P(T=1, R=0, L=0)$. Show your work. | + | |

- | # [10 points] Compute $P(T=1 | R=0, L=0)$. Show your work. | + | |

== Report == | == Report == |