# \[ANN\] Turing.jl 0.14.0

**URL:** <https://discourse.julialang.org/t/ann-turing-jl-0-14-0/45520>\
**Category:** Probabilistic Programming\
**Created:** [August 25, 2020, 5:16pm UTC](https://discourse.julialang.org/t/ann-turing-jl-0-14-0/45520 "2020-08-25T17:16:46Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![cpfiffer](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/cpfiffer/32/208747_2.png) [@cpfiffer](https://discourse.julialang.org/u/cpfiffer)\
**Post date:** [August 25, 2020, 5:16pm UTC](https://discourse.julialang.org/t/ann-turing-jl-0-14-0/45520/1 "2020-08-25T17:16:47Z")

</div>

Hey folks,

It’s been a while since our last release, which we’re hoping to avoid entirely. The Turing-verse is mostly adapting [Colprac](https://github.com/SciML/ColPrac) going forward, which means we’ll be flagging releases much more frequently to keep up with bug fixes and minor changes.

There are a _lot_ of things wrapped up in this release. The biggest thing we want to note is that **Turing is dropping support for Julia \< 1.3** , so please update to 1.3 or greater to continue to receive the most current updates. We dropped 1.3 for a handful of reasons, primarily that necessary fixes for some Turing dependencies had some requirement for 1.3+.

I’ll list out the major features and improvements you can expect with 0.14. There were a ton of bug fixes and miscellaneous improvements to the whole Turingverse, too many to list here, so I’ll focus on the primary changes to the system.

## Affine invariant ensemble sampling (`emcee`)

You may be familiar with the excellent [`emcee` package](https://github.com/dfm/emcee) in Python, which implements affine invariant ensemble sampling. We added `emcee`-style sampling to AdvancedMH recently, and Turing 0.14 allows users to sample Turing models with the ensemble sampler.

Usage:

```julia
using Turing

@model gdemo(x, y) = begin
    s ~ InverseGamma(2,3)
    m ~ Normal(0, sqrt(s))

    x ~ Normal(m, sqrt(s))
    y ~ Normal(m, sqrt(s))
end

n_samples = 100
n_walkers = 1000

spl = Turing.Inference.Emcee(n_walkers, MvNormal(2, 100), 2.0)
model = gdemo(1.5, 2.0)

chain1 = sample(model, spl, n_samples)

```

There’s two caveats with this particular sampling method:

1. Each “chain” represents a walker.
2. You cannot use Gibbs with `Emcee`.

As per usual, give it a try and let us know how it works.

## MLE/MAP fixes

You can now provide a starting point for MLE/MAP, as in

```julia
estimate = optimize(model, MLE(), LBFGS(), starting_point)

```

Second-order optimizers like `Newton` are now broken, in favor of first-order methods using the AD-derived gradient. The previous version of MLE/MAP used finite difference gradients instead of the available AD gradient, which is now fixed.

## AdvancedVI.jl

The variational inference library has been spun off into [AdvancedVI.jl](https://github.com/TuringLang/AdvancedVI.jl) to mirror the various satellite packages that Turing has on offer. Expect future development on VI to show up there.