15,763 research outputs found
Pseudo-Marginal Bayesian Inference for Gaussian Processes
The main challenges that arise when adopting Gaussian Process priors in
probabilistic modeling are how to carry out exact Bayesian inference and how to
account for uncertainty on model parameters when making model-based predictions
on out-of-sample data. Using probit regression as an illustrative working
example, this paper presents a general and effective methodology based on the
pseudo-marginal approach to Markov chain Monte Carlo that efficiently addresses
both of these issues. The results presented in this paper show improvements
over existing sampling methods to simulate from the posterior distribution over
the parameters defining the covariance function of the Gaussian Process prior.
This is particularly important as it offers a powerful tool to carry out full
Bayesian inference of Gaussian Process based hierarchic statistical models in
general. The results also demonstrate that Monte Carlo based integration of all
model parameters is actually feasible in this class of models providing a
superior quantification of uncertainty in predictions. Extensive comparisons
with respect to state-of-the-art probabilistic classifiers confirm this
assertion.Comment: 14 pages double colum
Variational Bayesian inference for linear and logistic regression
The article describe the model, derivation, and implementation of variational
Bayesian inference for linear and logistic regression, both with and without
automatic relevance determination. It has the dual function of acting as a
tutorial for the derivation of variational Bayesian inference for simple
models, as well as documenting, and providing brief examples for the
MATLAB/Octave functions that implement this inference. These functions are
freely available online.Comment: 28 pages, 6 figure
A Logical Foundation for Environment Classifiers
Taha and Nielsen have developed a multi-stage calculus {\lambda}{\alpha} with
a sound type system using the notion of environment classifiers. They are
special identifiers, with which code fragments and variable declarations are
annotated, and their scoping mechanism is used to ensure statically that
certain code fragments are closed and safely runnable. In this paper, we
investigate the Curry-Howard isomorphism for environment classifiers by
developing a typed {\lambda}-calculus {\lambda}|>. It corresponds to
multi-modal logic that allows quantification by transition variables---a
counterpart of classifiers---which range over (possibly empty) sequences of
labeled transitions between possible worlds. This interpretation will reduce
the "run" construct---which has a special typing rule in
{\lambda}{\alpha}---and embedding of closed code into other code fragments of
different stages---which would be only realized by the cross-stage persistence
operator in {\lambda}{\alpha}---to merely a special case of classifier
application. {\lambda}|> enjoys not only basic properties including subject
reduction, confluence, and strong normalization but also an important property
as a multi-stage calculus: time-ordered normalization of full reduction. Then,
we develop a big-step evaluation semantics for an ML-like language based on
{\lambda}|> with its type system and prove that the evaluation of a well-typed
{\lambda}|> program is properly staged. We also identify a fragment of the
language, where erasure evaluation is possible. Finally, we show that the proof
system augmented with a classical axiom is sound and complete with respect to a
Kripke semantics of the logic
- …