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BOPP: Bayesian Optimization for Probabilistic Programs

See our NIPS Spotlight video for TLDR.

Latest release: bopp "0.1.5"

BOPP is a package for automated marginal maximum a posteriori inference (MMAP) based around the probabilistic programming system Anglican. The user only needs to write their model in the same manner as existing Anglican programs and by using the defopt construct instead of defquery, select the variables to be optimized, with the rest marginalized out. It can also be used as a means of exploiting the target source code to improve Bayesian optimization, delivering things such as automatic domain scaling, unbounded optimization, and implicit constraint satisfaction including equality constraints. The key idea is to use a series of code transformations to extract from the original program all the things that are needed to carry out the MMAP problem, such as the target function itself and a program for optimizing the acquisition function subject to the implicit constraints. These are then passed to our other package Deodorant, which uses these to solve the problem probabilistic programs creates for BO. The following paper should be referred to for full algorithmic details and we ask that you cite this paper if you use BOPP in your work.

Rainforth, T., Le, T. A., van de Meent, J.-W., Osborne, M. A., & Wood, F. (2016). Bayesian Optimization for Probabilistic Programs. In Advances in Neural Information Processing Systems.

    title = {Bayesian Optimization for Probabilistic Programs},
    author = {Rainforth, Tom and Le, Tuan Anh and van de Meent, Jan-Willem and Osborne, Michael A and Wood, Frank},
    booktitle = {Advances in Neural Information Processing Systems 29},
    pages = {280--288},
    year = {2016},
    url = {}


To use BOPP in your own Leiningen projects, just include the dependency in your project.clj:

(defproject foo
  :dependencies [...
                 [bopp "0.1.5"]

In your Clojure files, remember to require functions from core.clj, e.g.:

(ns bar
  (require [bopp.core :refer :all]))

The full documentation can be found here. Checkout core/defopt and core/doopt in particular.

Though BOPP currently runs of a snapshot of Anglican that means you don't need to install Anglican explicitly, is does have the same requirements in terms of java, Leiningen etc and so we refer the reader to and recommend that users follow section 2 in the user start up guide. We also recommend that you familiarize yourself on the syntax of Anglican through the provided link, as this is the same syntax as Anglican, with the addition of the forms defopt and doopt.


Defining target programs

BOPP targets are specified using the macro defopt. This is identical to defquery in Anglican except that it takes as an extra input of the target variables to be optimized. BOPP will optimize the evidence of the program with respect to the variables, whilst marginalizing out over the others. For example:

(defopt simple-bimodal [y] [theta]
  (let [x (sample (normal 0 1))
        theta (sample (normal x 0.5))]
    (observe (normal (sqrt (* theta theta)) 0.5) y)))

specifies a model where we wish to optimize theta, marginalizing out x.

There are a couple of small restrictions on the programs that can be specified to ensure that they constitute valid target programs, namely:

  1. Each target variable must be bound to a value directly by a sample statement with fixed measure-type distribution argument (i.e. not a weird defdist distribution object).
  2. The program must be written such that any possible execution trace binds each optimization variable exactly once.
  3. Although any target variable may be lexically multiply bound, it must have the same base measure in all possible execution traces.

Don't worry, if this doesn't make too much sense, BOPP catches violations of these automatically and gives you and error telling you what you violated. Please see the paper and supplementary material for more information.

Running BOPP

Calling BOPP is super simple, just call

(doopt algorithm opt-query opt-query-args num-samples args)


  • algorithm = Inference algorithm used for estimating the marginal [:smc, :pcascade, :importance]
  • opt-query = Query to be optimized, defined by defopt.
  • opt-query-args = Fixed inputs of opt-query-args (y in earlier example)
  • num-samples = Number of samples for constructing the estimator of the marginal.
  • args = Optional arguments as key-value pairs. See docstring for doopt. Note that, by default, the arguments are setup for speed rather per iteration performance. If you wish to run bopp as per the setup in the paper, or have a problem where the inference is particularly expensive, you should include in the option list :speed-option :careful.

This returns a lazy infinite sequence of samples. Call take on this will convert it to a fixed number of outputs. Note that as BOPP is a GP based BO scheme, scaling in N is quite poor and practically limited to around 400-500.


A number of worksheets are provided to give example usage in different cases. These can be accessed in a Gorilla REPL by running lein gorilla from the base folder and going through worksheets in the worksheets/ folder. You can also see (but not run) an example worksheet without installing BOPP here.


Copyright © Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael Osborne and Frank Wood

BOPP is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

BOPP is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with BOPP. If not, see