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a/src/modules/glm/samplers/KS.h | b/src/modules/glm/samplers/KS.h | ||
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2 | #define KS_H_ |
2 | #define KS_H_ |

3 | 3 | ||

4 | class RNG; |
4 | class RNG; |

5 | 5 | ||

6 | namespace glm { |
6 | namespace glm { |

7 | /** |
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8 | * Utility function used by the HolmesHeld and AlbertChib sampling |
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9 | * methods in binary logistic regression models. |
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10 | * |
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11 | * A variable "Z" with logistic distribution may be considered as |
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12 | * a scale mixture of normal distributions with variance lambda, |
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13 | * where lambda=(2*psi)^2 and "psi" has a Kolmogorov-Smirnov |
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14 | * distribution. Given Z, this function draws a sample from the |
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15 | * posterior distribution of lambda. |
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16 | * |
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17 | * Although the posterior distribution of lambda given Z cannot |
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18 | * be expressed in closed form, it can be efficiently sampled using |
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19 | * a series approximation as described by Devroye (1986) Non-Uniform |
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20 | * Random Variate Generation, Springer-Verlag, New York. Note that |
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21 | * this book is currently available for free on line at |
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22 | * http://luc.devroye.org/rnbookindex.html |
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23 | * |
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24 | * @param Z value of a random variable with logistic distribution |
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25 | * |
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26 | * @param rng Random number generator used for sampling |
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27 | */ |
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7 | ```
double sample_lambda(double delta, RNG *rng);
``` |
28 | ```
double sample_lambda(double Z, RNG *rng);
``` |

8 | } |
29 | } |

9 | 30 | ||

10 | #endif /* KS_H_ */ |
31 | #endif /* KS_H_ */ |