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1018
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1 OVERVIEW
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2 ========
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3
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4 Before we start defining what a sampler is and how it should be defined in
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5 pylearn, we should first know what we're up against.
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6
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7 The workflow I have in mind is the following:
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8 1. identify the most popular sampling algorithms in the litterature
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9 2. get up to speed with methods we're not familiar with
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10 3. identify common usage patterns, properties of the algorithm, etc.
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11 4. decide on an API / best way to implement them
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12 5. prioritize the algorithms
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13 6. code away
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14
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15 1.BACKGROUND
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16 =============
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17
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18 This section should provide a brief overview of what exists in the litterature.
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19 We should make sure to have a decent understanding of all of these (not everyone
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20 has to be experts though), so that we can *intelligently* design our sampler
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21 interface based on common usage patterns, properties, etc.
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22
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23 Sampling from basic distributions
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24 * already supported: uniform, normal, binomial, multinomial
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25 * wish list: beta, poisson, others ?
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26
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27 List of sampling algorithms:
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28
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29 * inversion sampling
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30 * rejection sampling
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31 * importance sampling
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32 * Markov Chain Monte Carlo
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33 * Gibbs sampling
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34 * Metropolis Hastings
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35 * Slice Sampling
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36 * Annealing
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37 * Parallel Tempering, Tempered Transitions, Simulated Tempering
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38 * Nested Sampling (?)
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39 * Hamiltonian Monte Carlo
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