1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146

extern crate rand;

use self::rand::Rng;
use data::DataOrSuffStat;
use std::fmt::Debug;
use std::marker::PhantomData;
use std::sync::Arc;
use traits::*;

/// A wrapper for a complete conjugate model
///
/// # Paramters
///
/// `X`: The type of the data/observations to be modeled
/// `Fx`: The type of the likelihood, *f(x|θ)*
/// `Pr`: The type of the prior on the parameters of `Fx`, π(θ)
pub struct ConjugateModel<X, Fx, Pr>
where
    X: Debug,
    Fx: Rv<X> + HasSuffStat<X>,
    Pr: ConjugatePrior<X, Fx>,
{
    /// Pointer to an `Rv` implementing `ConjugatePrior` for `Fx`
    prior: Arc<Pr>,
    /// A `SuffStat` for `Fx`
    suffstat: Fx::Stat,
    _phantom: PhantomData<X>,
}

impl<X, Fx, Pr> ConjugateModel<X, Fx, Pr>
where
    X: Debug,
    Fx: Rv<X> + HasSuffStat<X>,
    Pr: ConjugatePrior<X, Fx>,
{
    /// Create a new conjugate model
    ///
    /// # Arguments
    ///
    /// *fx*:
    ///
    /// # Example
    ///
    /// ```
    /// # extern crate rv;
    /// use std::sync::Arc;
    /// use rv::prelude::*;
    /// use rv::ConjugateModel;
    ///
    /// let pr = Arc::new(Beta::jeffreys());
    /// let fx = Bernoulli::uniform();
    /// let model = ConjugateModel::<bool, Bernoulli, Beta>::new(&fx, pr);
    /// ```
    pub fn new(fx: &Fx, pr: Arc<Pr>) -> Self {
        ConjugateModel {
            prior: pr,
            suffstat: fx.empty_suffstat(),
            _phantom: PhantomData,
        }
    }

    /// Log marginal likelihood, *f(obs)*
    pub fn ln_m(&self) -> f64 {
        self.prior.ln_m(&self.obs())
    }

    /// Log posterior predictive, *f(y|obs)*
    pub fn ln_pp(&self, y: &X) -> f64 {
        self.prior.ln_pp(&y, &self.obs())
    }

    /// Return the posterior distribution
    /// # Example
    ///
    /// ```
    /// # extern crate rv;
    /// use std::sync::Arc;
    /// use rv::prelude::*;
    /// use rv::ConjugateModel;
    ///
    /// let flips: Vec<bool> = vec![true, false, true, false, false, false];
    ///
    /// let pr = Arc::new(Beta::new(1.0, 1.0).unwrap());
    /// let fx = Bernoulli::uniform();
    /// let mut model = ConjugateModel::<bool, Bernoulli, Beta>::new(&fx, pr);
    ///
    /// model.observe_many(&flips);
    ///
    /// let post = model.posterior();
    ///
    /// assert_eq!(post, Beta { alpha: 3.0, beta: 5.0 });
    /// ```
    pub fn posterior(&self) -> Pr::Posterior {
        self.prior.posterior(&self.obs())
    }

    /// Return the observations
    fn obs(&self) -> DataOrSuffStat<X, Fx> {
        DataOrSuffStat::SuffStat(&self.suffstat)
    }
}

impl<X, Fx, Pr> SuffStat<X> for ConjugateModel<X, Fx, Pr>
where
    X: Debug,
    Fx: Rv<X> + HasSuffStat<X>,
    Pr: ConjugatePrior<X, Fx>,
{
    fn n(&self) -> usize {
        self.suffstat.n()
    }

    fn observe(&mut self, x: &X) {
        self.suffstat.observe(&x);
    }

    fn forget(&mut self, x: &X) {
        self.suffstat.forget(&x);
    }
}

impl<X, Fx, Pr> Rv<X> for ConjugateModel<X, Fx, Pr>
where
    X: Debug,
    Fx: Rv<X> + HasSuffStat<X>,
    Pr: ConjugatePrior<X, Fx>,
{
    fn ln_f(&self, x: &X) -> f64 {
        self.prior.ln_pp(&x, &self.obs())
    }

    fn draw<R: Rng>(&self, mut rng: &mut R) -> X {
        let post = self.posterior();
        let fx: Fx = post.draw(&mut rng);
        fx.draw(&mut rng)
    }

    fn sample<R: Rng>(&self, n: usize, mut rng: &mut R) -> Vec<X> {
        let post = self.posterior();
        (0..n)
            .map(|_| {
                let fx: Fx = post.draw(&mut rng);
                fx.draw(&mut rng)
            }).collect()
    }
}