28,935 research outputs found
The Infinite Latent Events Model
We present the Infinite Latent Events Model, a nonparametric hierarchical Bayesian distribution over infinite dimensional Dynamic Bayesian Networks with binary state representations and noisy-OR-like transitions. The distribution can be used to learn structure in discrete timeseries data by simultaneously inferring a set of latent events, which events fired at each timestep, and how those events are causally linked. We illustrate the model on a sound factorization task, a network topology identification task, and a video game task.NTT Communication Science LaboratoriesUnited States. Air Force Office of Scientific Research (AFOSR FA9550-07-1-0075)United States. Office of Naval Research (ONR N00014-07-1-0937)National Science Foundation (U.S.) (Graduate Research Fellowship)United States. Army Research Office (ARO W911NF-08-1-0242)James S. McDonnell Foundation (Causal Learning Collaborative Initiative
A nonparametric HMM for genetic imputation and coalescent inference
Genetic sequence data are well described by hidden Markov models (HMMs) in
which latent states correspond to clusters of similar mutation patterns. Theory
from statistical genetics suggests that these HMMs are nonhomogeneous (their
transition probabilities vary along the chromosome) and have large support for
self transitions. We develop a new nonparametric model of genetic sequence
data, based on the hierarchical Dirichlet process, which supports these self
transitions and nonhomogeneity. Our model provides a parameterization of the
genetic process that is more parsimonious than other more general nonparametric
models which have previously been applied to population genetics. We provide
truncation-free MCMC inference for our model using a new auxiliary sampling
scheme for Bayesian nonparametric HMMs. In a series of experiments on male X
chromosome data from the Thousand Genomes Project and also on data simulated
from a population bottleneck we show the benefits of our model over the popular
finite model fastPHASE, which can itself be seen as a parametric truncation of
our model. We find that the number of HMM states found by our model is
correlated with the time to the most recent common ancestor in population
bottlenecks. This work demonstrates the flexibility of Bayesian nonparametrics
applied to large and complex genetic data
The interplay of microscopic and mesoscopic structure in complex networks
Not all nodes in a network are created equal. Differences and similarities
exist at both individual node and group levels. Disentangling single node from
group properties is crucial for network modeling and structural inference.
Based on unbiased generative probabilistic exponential random graph models and
employing distributive message passing techniques, we present an efficient
algorithm that allows one to separate the contributions of individual nodes and
groups of nodes to the network structure. This leads to improved detection
accuracy of latent class structure in real world data sets compared to models
that focus on group structure alone. Furthermore, the inclusion of hitherto
neglected group specific effects in models used to assess the statistical
significance of small subgraph (motif) distributions in networks may be
sufficient to explain most of the observed statistics. We show the predictive
power of such generative models in forecasting putative gene-disease
associations in the Online Mendelian Inheritance in Man (OMIM) database. The
approach is suitable for both directed and undirected uni-partite as well as
for bipartite networks
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