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View zoo
//
// Zoo: Infer prevalence of 3 animals after a zoo visit
//
// Observed: 3 lions, 2 tigers and 1 bear
//
// Questions:
// - Prevalence of each species
// - Probability of seeing a bear next time
//
View game_of_ur
//
// Game of Ur
//
var roll = function() {
return flip() + flip() + flip() + flip();
}
var game = function() {
var n = sample(RandomInteger({n: 20}));
View second_tallest
var person = function() {
var is_female = sample(Bernoulli({p: 0.51}))
if (is_female == false) {
return {hgt: sample(Gaussian({mu: 178.0, sigma: 7.7})), is_female: is_female}
} else {
return {hgt: sample(Gaussian({mu: 163.0, sigma: 7.3})), is_female: is_female}
}
}
var is_taller = function(h1, h2) {
View four_dice.wppl
// 4 dice: 4, 6, 8 and 12 sides
// each dice is perfect
var dice = [
Categorical(
{
ps: [1/4, 1/4, 1/4, 1/4],
vs: [0, 1, 2, 3]
}
),
Categorical(
@usptact
usptact / monty.wppl
Last active Aug 26, 2018
Agentmodels, WebPPL: Monty Hall Example
View monty.wppl
// Remove each element in array ys from array xs
var remove = function(xs, ys) {
return _.without.apply(null, [xs].concat(ys));
};
var doors = [1, 2, 3];
// Monty chooses a door that is neither Alice's door
// nor the prize door
var monty = function(aliceDoor, prizeDoor) {
@usptact
usptact / softrank_distribution
Last active Jul 9, 2019
SoftRank: from scores to rank distributions
View softrank_distribution
"""
Demo script to compute rank distributions given pairwise preference probabilities.
Data:
- pairwise preference probabilities (not including self)
Output:
- distribution over ranks
Michael Taylor, John Guiver, Stephen Robertson and Tom Minka, "SoftRank: Optimising Non-Smooth Rank Metrics", WSDM 2008.
@usptact
usptact / letor_metrics.py
Created Dec 6, 2017 — forked from mblondel/letor_metrics.py
Learning to rank metrics.
View letor_metrics.py
# (C) Mathieu Blondel, November 2013
# License: BSD 3 clause
import numpy as np
def ranking_precision_score(y_true, y_score, k=10):
"""Precision at rank k
Parameters
View IntervalSort
"""
Class to sort intervals.
A collection of intervals is sorted:
(1) in increasing order by start index
(2) in increasing order by end index
Sample data:
data = [(1, 2), (0, 1), (0, 3), (5, 9), (1, 4), (1, 2), (2, 5), (5, 8), (3, 6)]
"""
View SegmentTiler
"""
Class to pack or tile segments.
Example data:
data = [
(0, 1), (0, 2), (0, 3), (1, 3), (2, 4), (0, 5)
]
NB: sorted segments are expected (cf. IntervalSort)!
"""
@usptact
usptact / dataphilly-jul2016.ipynb
Created Jan 26, 2017 — forked from AustinRochford/dataphilly-jul2016.ipynb
DataPhilly July 2016 Introduction to Probabilistic Programming
View dataphilly-jul2016.ipynb
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