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Published: Jul 27, 2020 | License: MIT

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Infergo — Go programs that learn

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.

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[more examples]

Learning parameters of the Normal distribution from observations.


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


// 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{
	L:   10,
	Eps: 0.1,
samples := make(chan []float64)
hmc.Sample(m, x, samples)
for i := 0; i != 1000; i++ {
	x = <-samples


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.