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annotate doc/v2_planning/sampler.txt @ 1169:3a1225034751

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author Pascal Lamblin <lamblinp@iro.umontreal.ca>
date Fri, 17 Sep 2010 14:29:49 -0400
parents 875d53754bd0
children 0e12ea6ba661
rev   line source
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1
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2 Inference / Sampling committee: JB, GD, AC
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4 OVERVIEW
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5 ========
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6
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7 Before we start defining what a sampler is and how it should be defined in
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8 pylearn, we should first know what we're up against.
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9
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10 The workflow I have in mind is the following:
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11 1. identify the most popular sampling algorithms in the litterature
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12 2. get up to speed with methods we're not familiar with
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13 3. identify common usage patterns, properties of the algorithm, etc.
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14 4. decide on an API / best way to implement them
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15 5. prioritize the algorithms
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16 6. code away
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17
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18 1.BACKGROUND
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19 =============
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20
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21 This section should provide a brief overview of what exists in the litterature.
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22 We should make sure to have a decent understanding of all of these (not everyone
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23 has to be experts though), so that we can *intelligently* design our sampler
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24 interface based on common usage patterns, properties, etc.
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25
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26 Sampling from basic distributions
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27 * already supported: uniform, normal, binomial, multinomial
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28 * wish list: beta, poisson, others ?
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29
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30 List of sampling algorithms:
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31 * inversion sampling
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32 * rejection sampling
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33 * importance sampling
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34 * Markov Chain Monte Carlo
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35 * Gibbs sampling
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36 * Metropolis Hastings
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37 * Slice Sampling
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38 * Annealing
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39 * Parallel Tempering, Tempered Transitions, Simulated Tempering
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40 * Nested Sampling (?)
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41 * Hamiltonian Monte Carlo --> or is it Hybrid Monte Carlo?
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42
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43 3. USAGE PATTERNS
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44 =================
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45
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46 * MCMC methods have a usage pattern that is quite different from the kind of univariate sampling methods
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47 needed for nice-and-easy parametric families.
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48
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49