1 / 6

Sequence Models for Prediction Problems: Model Selection Guide

This guide covers identifying the most suitable models for various prediction tasks using sequence models such as Markov Models and Hidden Markov Models (HMMs). It includes examples of predicting states, estimating parameters, Laplace smoothing, and computing stationary distributions and probabilities.

morley
Download Presentation

Sequence Models for Prediction Problems: Model Selection Guide

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Homework 7: Sequence Models

  2. 1. What model is most appropriate? For each prediction problem below, what model (e.g., linear regression, linear classifier, HMM, particle filter) would you use?

  3. 2. Predicting states in a Markov Model 0.6 0.2 0.4 • Suppose the weather starts out in a Sunny state. What is the probability that it is Sunny • One time step later • Two time steps later • Three time steps later • Suppose the weather starts out with a 0.7 probability of Sunny, and 0.3 probability of Rainy. Now, what is the probability that it is Sunny • One time step later • Two time steps later • Three time steps later Sunny Rainy 0.8

  4. 3. Estimating parameters of a MM ? ? Initial state is Sunny: ? is Rainy: ? ? • For each sequence of (S)unny and (R)ainy below, determine the corresponding parameters of the MM using maximum likelihood. • RRRSRRSRRRRR • SRSSRSRSSRSRS • For the sequence in (a) above, estimate the parameters for the MM again, but this time use Laplace smoothing with k=1. Sunny Rainy ?

  5. 4. Finding the stationary distribution Determine the stationary distribution for the two MMs on the right. 0.6 0.2 0.4 Sunny Rainy 0.8 0.8 0.8 0.2 Sunny Rainy 0.2

  6. 5. HMM Computations Notice that the parameters of this HMM do not match the slides from lecture. • Compute P(H1=Sleep | O1=Ans) • Compute P(H2=Sleep | O2=-Ans) H1 H2 O1 O2

More Related