kdetrees: non-parametric estimation of phylogenetic tree distributions

Kdetrees: Non-parametric Estimation of Phylogenetic Tree Distributions

MOTIVATION Although the majority of gene histories found in a clade of organisms are expected to be generated by a common process (e.g. the coalescent process), it is well known that numerous other coexisting processes (e.g. horizontal gene transfers, gene duplication and subsequent neofunctionalization) will cause some genes to exhibit a history distinct from those of the majority of genes. Su...

Non-parametric Estimation of Probability Distributions from Sampled Signals

This paper is concerned with the non-parametric estimation of probability distributions from band-limited and at least critically sampled signals such as images. Conventional approaches employing histograms or Parzen windows often perform unsatisfactorily since they ignore two important properties of such signals, namely that they are ordered and contain sufficient information to reconstruct th...

Tutorial on Phylogenetic Tree Estimation

1 Tutorial Summary All biological disciplines are united by the idea that species share a common history. The genealogical history of life-also called an \evolutionary tree"-is usually represented by a bifurcating, leaf-labeled tree. The use of evolutionary trees is a fundamental step in many biological problems, such as multiple sequence alignments, protein structure and function prediction, a...

Generalized EM estimation for semi-parametric mixture distributions with discretized non-parametric component

We consider independent sampling from a two-component mixture distribution, where one component (called the parametric component) is from a known distributional family and the other component (called the non-parametric component) is unknown. This is a semi-parametric mixture distribution. We discretize the non-parametric component and estimate the parameters of this mixture model, namely the mi...

Non–parametric volatility estimation