infergo is a probabilistic programming facility for the Go language. infergo allows to write probabilistic models in almost unrestricted Go and relies on automatic differentiation for optimization and inference. Works anywhere where Go does. Hosted on Bitbucket. Licensed under the MIT license.

Example

more examples

Learning parameters of the Normal distribution from observations:

Model

type Model struct {
    Data []float64
}

// x[0] is the mean, x[1] is the log stddev of the distribution
func (m *Model) Observe(x []float64) float64 {
    // Our prior is a unit normal ...
    ll := Normal.Logps(0, 1, x...)
    // ... but the posterior is based on data observations.
	ll += Normal.Logps(x[0], math.Exp(x[1]), m.Data...)
    return ll
}

Inference

// Data
m := &Model{[]float64{
	-0.854, 1.067, -1.220, 0.818, -0.749,
	0.805, 1.443, 1.069, 1.426, 0.308}}

// Parameters
mean, logs := 0, 0
x := []float64{mean, logs}
	
// Optimiziation
opt := &infer.Momentum{
    Rate:  0.01,
    Decay: 0.998,
}
for iter := 0; iter != 1000; iter++ {
    opt.Step(m, x)
}
mean, logs = x[0], x[1]

// Posterior
hmc := &infer.HMC{
	Eps: 0.1,
}
samples := make(chan []float64)
hmc.Sample(m, x, samples)
for i := 0; i != 1000; i++ {
	x = <-samples
}
hmc.Stop()

Acknowledgements

I owe a debt of gratitude to Frank Wood who introduced me to probabilistic programming and inspired me to pursue probabilistic programming paradigms and applications. I also want to thank Jan-Willem van de Meent, with whom I had fruitful discussions of motives, ideas, and implementation choices behind infergo, and whose thoughts and recommendations significantly influenced infergo design. Finally, I want to thank PUB+, the company I work for, for supporting me in development of infergo and letting me experiment with applying probabilistic programming to critical decision-making in production environment.