`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

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.