Caution
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Bayesian Statistics: Mixture Models
Oren Bochman
Mixture Models, Homework, Computational Considerations, MCMC
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---
title : 'Computational considerations for Mixture Models'
subtitle : 'Bayesian Statistics: Mixture Models'
categories:
- Bayesian Statistics
keywords:
- Mixture Models
- Homework
- Computational Considerations
- MCMC
---
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1. Consider a mixture of three Gaussian distribution with common identity covariance matrix and means
$$
\begin{aligned}
\mu_1 &= (0,0)' \\
\mu_2 &= (1/3,1/3)' \\
\mu_3 &= (-2/3,1/3)'
\end{aligned}
$$
For an observation $x_i = (31,−23)'$ what is the value of $v_{i,2}$, the probability of the observation being generated by the second component (rounded to three decimal places)?
- [x] 0.928
- [ ] 1.000
- [ ] 0.072
2. True or False: The starting value for the parameters of the mixture model in the EM algorithm could have an impact on the solution you obtain.
- [x] True
- [ ] False
3. True or False: Consider a Bayesian formulation of a Mixture Model that uses informative priors for all the parameters. A Markov chain Monte Carlo (MCMC) algorithm for fitting such model will fail to work if no observations are allocated to a component of the mixture.
- [ ] True
- [x] False
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