67 research outputs found
A New Approach to Probabilistic Programming Inference
We introduce and demonstrate a new approach to inference in expressive
probabilistic programming languages based on particle Markov chain Monte Carlo.
Our approach is simple to implement and easy to parallelize. It applies to
Turing-complete probabilistic programming languages and supports accurate
inference in models that make use of complex control flow, including stochastic
recursion. It also includes primitives from Bayesian nonparametric statistics.
Our experiments show that this approach can be more efficient than previously
introduced single-site Metropolis-Hastings methods.Comment: Updated version of the 2014 AISTATS paper (to reflect changes in new
language syntax). 10 pages, 3 figures. Proceedings of the Seventeenth
International Conference on Artificial Intelligence and Statistics, JMLR
Workshop and Conference Proceedings, Vol 33, 201
Core Precession and Global Modes in Granular Bulk Flow
A transition from local to global shear zones is reported for granular flows
in a modified Couette cell. The experimental geometry is a slowly rotating drum
which has a stationary disc of radius R_s fixed at its bottom. Granular
material, which fills this cell up to height H, forms a wide shear zone which
emanates from the discontinuity at the stationary discs edge. For shallow
layers (H/R_s < 0.55), the shear zone reaches the free surface, with the core
of the material resting on the disc and remaining stationary. In contrast, for
deep layers (H/R_s > 0.55), the shear zones meet below the surface and the core
starts to precess. A change in the symmetry of the surface velocities reveals
that this behavior is associated with a transition from a local to a global
shear mode.Comment: 4 pages, 7 figures, submitte
Bayesian Optimization for Probabilistic Programs
We present the first general purpose framework for marginal maximum a
posteriori estimation of probabilistic program variables. By using a series of
code transformations, the evidence of any probabilistic program, and therefore
of any graphical model, can be optimized with respect to an arbitrary subset of
its sampled variables. To carry out this optimization, we develop the first
Bayesian optimization package to directly exploit the source code of its
target, leading to innovations in problem-independent hyperpriors, unbounded
optimization, and implicit constraint satisfaction; delivering significant
performance improvements over prominent existing packages. We present
applications of our method to a number of tasks including engineering design
and parameter optimization
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