Nonparametric Combinatorial Sequence Models

Nonparametric Combinatorial Sequence Models

This work considers biological sequences that exhibit combinatorial structures in their composition: groups of positions of the aligned sequences are "linked" and covary as one unit across sequences. If multiple such groups exist, complex interactions can emerge between them. Sequences of this kind arise frequently in biology but methodologies for analyzing them are still being developed. This ...

Combinatorial stochastic processes and nonparametric Bayesian modeling

Nonparametric Transfer Function Models.

In this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between 'input' and 'output' time series. The transfer function is smooth with unknown functional forms, and the noise is assumed to be a stationary autoregressive-moving average (ARMA) process. The nonparametric transfer function is estimated jointly with the ARMA parameters. By modelin...

Bayesian Nonparametric Models

‘ We have been looking at models that posit latent structure in high dimensional data. We use the posterior to uncover that structure. ‘ The two main types are mixtures (and variants, like mixed-membership) and factor models (like PCA, factor analysis, and others). ‘ A nagging concern for these methods is model selection—how do I choose the number of mixture components? the number of factors? ‘...

Spatial Nonparametric Bayesian Models

The prior distribution is an essential ingredient of any Bayesian analysis, and it plays a major role in determining the final results. As such, Bayesians attempt to use prior distributions that have certain properties. Perhaps the main property is a desire to accurately reflect prior information, i.e., information external to the experiment at hand. We would supplement this vague property with...