2,893 research outputs found
An optimal data ordering scheme for Dirichlet process mixture models
In recent years, there has been increasing interest in Bayesian nonparametric methods due to their flexibility, and the availability of Markov chain Monte Carlo (MCMC) methods for sampling from the posterior distribution. As MCMC methods are generally time consuming for computation, there is a need for faster methods, which can be executed within a matter of seconds. A fast alternative to MCMC for sampling the well known and widely used Dirichlet process mixture (DPM) model is investigated to draw approximate independent and identically distributed samples from the posterior distribution of the latent allocations, and then to draw samples from the weights and locations conditional on the allocations. To address the order depend issue of the proposed algorithm, an optimal ordering scheme based on a sequence of optimizations is proposed to first obtain an optimal order of the data, and then run the algorithm on this ordering. The fast sampling algorithm is assisted by parallel computing using commands within MATLA
Approximate Decentralized Bayesian Inference
This paper presents an approximate method for performing Bayesian inference
in models with conditional independence over a decentralized network of
learning agents. The method first employs variational inference on each
individual learning agent to generate a local approximate posterior, the agents
transmit their local posteriors to other agents in the network, and finally
each agent combines its set of received local posteriors. The key insight in
this work is that, for many Bayesian models, approximate inference schemes
destroy symmetry and dependencies in the model that are crucial to the correct
application of Bayes' rule when combining the local posteriors. The proposed
method addresses this issue by including an additional optimization step in the
combination procedure that accounts for these broken dependencies. Experiments
on synthetic and real data demonstrate that the decentralized method provides
advantages in computational performance and predictive test likelihood over
previous batch and distributed methods.Comment: This paper was presented at UAI 2014. Please use the following BibTeX
citation: @inproceedings{Campbell14_UAI, Author = {Trevor Campbell and
Jonathan P. How}, Title = {Approximate Decentralized Bayesian Inference},
Booktitle = {Uncertainty in Artificial Intelligence (UAI)}, Year = {2014}
Optimal client recommendation for market makers in illiquid financial products
The process of liquidity provision in financial markets can result in
prolonged exposure to illiquid instruments for market makers. In this case,
where a proprietary position is not desired, pro-actively targeting the right
client who is likely to be interested can be an effective means to offset this
position, rather than relying on commensurate interest arising through natural
demand. In this paper, we consider the inference of a client profile for the
purpose of corporate bond recommendation, based on typical recorded information
available to the market maker. Given a historical record of corporate bond
transactions and bond meta-data, we use a topic-modelling analogy to develop a
probabilistic technique for compiling a curated list of client recommendations
for a particular bond that needs to be traded, ranked by probability of
interest. We show that a model based on Latent Dirichlet Allocation offers
promising performance to deliver relevant recommendations for sales traders.Comment: 12 pages, 3 figures, 1 tabl
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
This paper presents a methodology for creating streaming, distributed
inference algorithms for Bayesian nonparametric (BNP) models. In the proposed
framework, processing nodes receive a sequence of data minibatches, compute a
variational posterior for each, and make asynchronous streaming updates to a
central model. In contrast to previous algorithms, the proposed framework is
truly streaming, distributed, asynchronous, learning-rate-free, and
truncation-free. The key challenge in developing the framework, arising from
the fact that BNP models do not impose an inherent ordering on their
components, is finding the correspondence between minibatch and central BNP
posterior components before performing each update. To address this, the paper
develops a combinatorial optimization problem over component correspondences,
and provides an efficient solution technique. The paper concludes with an
application of the methodology to the DP mixture model, with experimental
results demonstrating its practical scalability and performance.Comment: This paper was presented at NIPS 2015. Please use the following
BibTeX citation: @inproceedings{Campbell15_NIPS, Author = {Trevor Campbell
and Julian Straub and John W. {Fisher III} and Jonathan P. How}, Title =
{Streaming, Distributed Variational Inference for Bayesian Nonparametrics},
Booktitle = {Advances in Neural Information Processing Systems (NIPS)}, Year
= {2015}
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
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