89,378 research outputs found
Evaluation of a Bayesian inference network for ligand-based virtual screening
Background
Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to enable a Bayesian inference network to be used for chemical similarity searching, where a database is ranked in order of decreasing probability of bioactivity.
Results
Bayesian inference networks were implemented using two different types of network and four different types of belief function. Experiments with the MDDR and WOMBAT databases show that a Bayesian inference network can be used to provide effective ligand-based screening, especially when the active molecules being sought have a high degree of structural homogeneity; in such cases, the network substantially out-performs a conventional, Tanimoto-based similarity searching system. However, the effectiveness of the network is much less when structurally heterogeneous sets of actives are being sought.
Conclusion
A Bayesian inference network provides an interesting alternative to existing tools for ligand-based virtual screening
Bayesian quantile regression: An application to the wage distribution in 1990s Britain
This paper illustrates application of Bayesian inference to quantile regression. Bayesian inference regards unknown parameters as random variables, and we describe an MCMC algorithm to estimate the posterior densities of quantile regression parameters. Parameter uncertainty is taken into account without relying on asymptotic approximations. Bayesian inference revealed effective in our application to the wage structure among working males in Britain between 1991 and 2001 using data from the British Household Panel Survey. Looking at different points along the conditional wage distribution uncovered important features of wage returns to education, experience and public sector employment that would be concealed by mean regression.quantile regression ; bayesian inference ; wage distribution ; MCMC
Robust approximate Bayesian inference
We discuss an approach for deriving robust posterior distributions from
-estimating functions using Approximate Bayesian Computation (ABC) methods.
In particular, we use -estimating functions to construct suitable summary
statistics in ABC algorithms. The theoretical properties of the robust
posterior distributions are discussed. Special attention is given to the
application of the method to linear mixed models. Simulation results and an
application to a clinical study demonstrate the usefulness of the method. An R
implementation is also provided in the robustBLME package.Comment: This is a revised and personal manuscript version of the article that
has been accepted for publication by Journal of Statistical Planning and
Inferenc
Bayesian inference for CoVaR
Recent financial disasters emphasised the need to investigate the consequence
associated with the tail co-movements among institutions; episodes of contagion
are frequently observed and increase the probability of large losses affecting
market participants' risk capital. Commonly used risk management tools fail to
account for potential spillover effects among institutions because they provide
individual risk assessment. We contribute to analyse the interdependence
effects of extreme events providing an estimation tool for evaluating the
Conditional Value-at-Risk (CoVaR) defined as the Value-at-Risk of an
institution conditioned on another institution being under distress. In
particular, our approach relies on Bayesian quantile regression framework. We
propose a Markov chain Monte Carlo algorithm exploiting the Asymmetric Laplace
distribution and its representation as a location-scale mixture of Normals.
Moreover, since risk measures are usually evaluated on time series data and
returns typically change over time, we extend the CoVaR model to account for
the dynamics of the tail behaviour. Application on U.S. companies belonging to
different sectors of the Standard and Poor's Composite Index (S&P500) is
considered to evaluate the marginal contribution to the overall systemic risk
of each individual institutio
On adaptive Bayesian inference
We study the rate of Bayesian consistency for hierarchical priors consisting
of prior weights on a model index set and a prior on a density model for each
choice of model index. Ghosal, Lember and Van der Vaart [2] have obtained
general in-probability theorems on the rate of convergence of the resulting
posterior distributions. We extend their results to almost sure assertions. As
an application we study log spline densities with a finite number of models and
obtain that the Bayes procedure achieves the optimal minimax rate
of convergence if the true density of the
observations belongs to the H\"{o}lder space . This
strengthens a result in [1; 2]. We also study consistency of posterior
distributions of the model index and give conditions ensuring that the
posterior distributions concentrate their masses near the index of the best
model.Comment: Published in at http://dx.doi.org/10.1214/08-EJS244 the Electronic
Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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}
- …