65,498 research outputs found
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Implicit probabilistic models are a flexible class of models defined by a
simulation process for data. They form the basis for theories which encompass
our understanding of the physical world. Despite this fundamental nature, the
use of implicit models remains limited due to challenges in specifying complex
latent structure in them, and in performing inferences in such models with
large data sets. In this paper, we first introduce hierarchical implicit models
(HIMs). HIMs combine the idea of implicit densities with hierarchical Bayesian
modeling, thereby defining models via simulators of data with rich hidden
structure. Next, we develop likelihood-free variational inference (LFVI), a
scalable variational inference algorithm for HIMs. Key to LFVI is specifying a
variational family that is also implicit. This matches the model's flexibility
and allows for accurate approximation of the posterior. We demonstrate diverse
applications: a large-scale physical simulator for predator-prey populations in
ecology; a Bayesian generative adversarial network for discrete data; and a
deep implicit model for text generation.Comment: Appears in Neural Information Processing Systems, 201
Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors
We investigate the application of hierarchical classification schemes to the
annotation of gene function based on several characteristics of protein
sequences including phylogenic descriptors, sequence based attributes, and
predicted secondary structure. We discuss three Bayesian models and compare
their performance in terms of predictive accuracy. These models are the
ordinary multinomial logit (MNL) model, a hierarchical model based on a set of
nested MNL models, and a MNL model with a prior that introduces correlations
between the parameters for classes that are nearby in the hierarchy. We also
provide a new scheme for combining different sources of information. We use
these models to predict the functional class of Open Reading Frames (ORFs) from
the E. coli genome. The results from all three models show substantial
improvement over previous methods, which were based on the C5 algorithm. The
MNL model using a prior based on the hierarchy outperforms both the
non-hierarchical MNL model and the nested MNL model. In contrast to previous
attempts at combining these sources of information, our approach results in a
higher accuracy rate when compared to models that use each data source alone.
Together, these results show that gene function can be predicted with higher
accuracy than previously achieved, using Bayesian models that incorporate
suitable prior information
Nonlinear Models Using Dirichlet Process Mixtures
We introduce a new nonlinear model for classification, in which we model the
joint distribution of response variable, y, and covariates, x,
non-parametrically using Dirichlet process mixtures. We keep the relationship
between y and x linear within each component of the mixture. The overall
relationship becomes nonlinear if the mixture contains more than one component.
We use simulated data to compare the performance of this new approach to a
simple multinomial logit (MNL) model, an MNL model with quadratic terms, and a
decision tree model. We also evaluate our approach on a protein fold
classification problem, and find that our model provides substantial
improvement over previous methods, which were based on Neural Networks (NN) and
Support Vector Machines (SVM). Folding classes of protein have a hierarchical
structure. We extend our method to classification problems where a class
hierarchy is available. We find that using the prior information regarding the
hierarchical structure of protein folds can result in higher predictive
accuracy
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Hierarchical classification for multiple, distributed web databases
The proliferation of online information resources increases the importance of effective and efficient distributed searching. Our research aims to provide an alternative hierarchical categorization and search capability based on a Bayesian network learning algorithm. Our proposed approach, which is grounded on automatic textual analysis of subject content of online web databases, attempts to address the database selection problem by first classifying web databases into a hierarchy of topic categories. The experimental results reported demonstrate that such a classification approach not only effectively reduces the class search space, but also helps to significantly improve the accuracy of classification performance
Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices
A methodological approach for modelling the spatial distribution of bioclimatic indices is proposed in this paper. The value of the bioclimatic index is modelled with a hierarchical Bayesian model that incorporates both structured and unstructured random effects. Selection of prior distributions is also discussed in order to better incorporate any possible prior knowledge about the parameters that could refer to the particular characteristics of bioclimatic indices. MCMC methods and distributed programming are used to obtain an approximation of the posterior distribution of the parameters and also the posterior predictive distribution of the indices. One main outcome of the proposal is the spatial bioclimatic probability distribution of each bioclimatic index, which allows researchers to obtain the probability of each location belonging to different bioclimates. The methodology is evaluated on two indices in the Island of Cyprus.Peer Reviewe
SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model
To realize human-like robot intelligence, a large-scale cognitive
architecture is required for robots to understand the environment through a
variety of sensors with which they are equipped. In this paper, we propose a
novel framework named Serket that enables the construction of a large-scale
generative model and its inference easily by connecting sub-modules to allow
the robots to acquire various capabilities through interaction with their
environments and others. We consider that large-scale cognitive models can be
constructed by connecting smaller fundamental models hierarchically while
maintaining their programmatic independence. Moreover, connected modules are
dependent on each other, and parameters are required to be optimized as a
whole. Conventionally, the equations for parameter estimation have to be
derived and implemented depending on the models. However, it becomes harder to
derive and implement those of a larger scale model. To solve these problems, in
this paper, we propose a method for parameter estimation by communicating the
minimal parameters between various modules while maintaining their programmatic
independence. Therefore, Serket makes it easy to construct large-scale models
and estimate their parameters via the connection of modules. Experimental
results demonstrated that the model can be constructed by connecting modules,
the parameters can be optimized as a whole, and they are comparable with the
original models that we have proposed
Identifying Clusters in Bayesian Disease Mapping
Disease mapping is the field of spatial epidemiology interested in estimating
the spatial pattern in disease risk across areal units. One aim is to
identify units exhibiting elevated disease risks, so that public health
interventions can be made. Bayesian hierarchical models with a spatially smooth
conditional autoregressive prior are used for this purpose, but they cannot
identify the spatial extent of high-risk clusters. Therefore we propose a two
stage solution to this problem, with the first stage being a spatially adjusted
hierarchical agglomerative clustering algorithm. This algorithm is applied to
data prior to the study period, and produces potential cluster structures
for the disease data. The second stage fits a separate Poisson log-linear model
to the study data for each cluster structure, which allows for step-changes in
risk where two clusters meet. The most appropriate cluster structure is chosen
by model comparison techniques, specifically by minimising the Deviance
Information Criterion. The efficacy of the methodology is established by a
simulation study, and is illustrated by a study of respiratory disease risk in
Glasgow, Scotland
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