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Found 15 papers, 0 papers with code
MCMC for continuous-time discrete-state systems
We propose a simple and novel framework for MCMC inference in continuous-time discrete-state systems with pure jump trajectories.
Searching for objects driven by context
The dominant visual search paradigm for object class detection is sliding windows.
Bayesian nonparametric models for ranked data
We develop a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items.
Learning Label Trees for Probabilistic Modelling of Implicit Feedback
User preferences for items can be inferred from either explicit feedback, such as item ratings, or implicit feedback, such as rental histories.
Scalable imputation of genetic data with a discrete fragmentation-coagulation process
We present a Bayesian nonparametric model for genetic sequence data in which a set of genetic sequences is modelled using a Markov model of partitions.
Modelling Genetic Variations using Fragmentation-Coagulation Processes
We propose a novel class of Bayesian nonparametric models for sequential data called fragmentation-coagulation processes (FCPs).
Gaussian process modulated renewal processes
In our experiments, we test these on a number of synthetic and real datasets.
Improvements to the Sequence Memoizer
The sequence memoizer is a model for sequence data with state-of-the-art performance on language modeling and compression.
Indian Buffet Processes with Power-law Behavior
The Indian buffet process (IBP) is an exchangeable distribution over binary matrices used in Bayesian nonparametric featural models.
Spatial Normalized Gamma Processes
We propose a simple and general framework to construct dependent DPs by marginalizing and normalizing a single gamma process over an extended space.
Dependent Dirichlet Process Spike Sorting
In this paper we propose a new incremental spike sorting model that automatically eliminates refractory period violations, accounts for action potential waveform drift, and can handle appearance" and "disappearance" of neurons.
An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering
We propose an efficient sequential Monte Carlo inference scheme for the recently proposed coalescent clustering model (Teh et al, 2008).
The Mondrian Process
We describe a novel stochastic process that can be used to construct a multidimensional generalization of the stick-breaking process and which is related to the classic stick breaking process described by Sethuraman1994 in one dimension.
A mixture model for the evolution of gene expression in non-homogeneous datasets
We address the challenge of assessing conservation of gene expression in complex, non-homogeneous datasets.
The Infinite Factorial Hidden Markov Model
We introduces a new probability distribution over a potentially infinite number of binary Markov chains which we call the Markov Indian buffet process.
