With the exception of Bayesian analysis, phylogenetic inference procedures typically identify a best estimate of phylogenetic relationships, a so called point estimate of the phylogeny. However, the point estimate is often relatively uninteresting in itself unless we have some measure of its reliability. This lecture will be about techniques for examining the robustness or significance of the r...

Machine learning is a science that explores the building and study of algorithms that can learn from the data. Machine learning process is the union of statistics and artificial intelligence and is closely related to computational statistics. Machine learning takes decisions based on the qualities of the studied data using statistics and adding more advanced artificial intelligence heuristics a...

Statistics) Bayesian Analysis and Computational Methods for Dynamic Modeling

In this contribution we take a look at the computational effort statistics as described by Koza. We transfer the notion from generational genetic programming to tournament-selection (steady-state) GP and show why, in both cases, the measured value of the effort often differs from its theoretical counterpart. It is discussed how systematic estimation errors are introduced by a low number of expe...

for any value of x in the interval (a, b). Chebyshev motivated his problem by investigation of limit theorems in probability theory, with some related work done even earlier by Heine. It was completely resolved by Markov; for more detailed comments and description of the related developments see Shohat and Tamarkin (1943), Akhiezer (1965) and Gautschi (1981). Here we will not describe the well ...

Given a large dataset and an estimation task, it is common to pre-process the data by reducing them to a set of sufficient statistics. This step is often regarded as straightforward and advantageous (in that it simplifies statistical analysis). I show that –on the contrary– reducing data to sufficient statistics can change a computationally tractable estimation problem into an intractable one. ...

1. Introduction. The spectacular growth in the field of computing science is obvious to all. Indeed, the most obvious manifestations, the ubiquitous microcomputer, is in some ways perhaps the least significant aspect of this revolution. The new pipeline and parallel architectures including systolic arrays and hypercubes, the emergence of artificial intelligence, the cheap availablility of RAM a...

Accelerating [HHC03]. acceleration [GS94]. Accept [CR98]. Accept-Reject [CR98]. Access [SH03]. Accompaniment [Rap01]. Accounting [HDM02]. Accurate [CJ02]. Acoustic [BMO01]. Adaptive [Bil00, DZ99, EHW03, GK97, Gou98, Gri99, MS03, Oeh98, PTP03, Pit02, Zha97]. Additive [EM02, KLH99, OR99, ST99]. Adjusted [DIR02]. After [Hub99]. Aided [HIT99]. Akaike [ST99]. Algorithm [CB01, CCFM01, GJ97, Glu00, HL...

Format: In the first part of the course, the instructors will give a broad overview of the relevant aspects of cellular biology, machine learning, and the engineering aspects of high-throughput experimentation. In the second part of the course, invited guests will give talks on more specialized aspects of the subject. Students will be required to i) carry out a computational data analysis of a ...

A new approach is described, for extracting and visualising structures in a data matrix Y in light of additional information BOTH about the ROWS in Y, given in matrix X, AND about the COLUMNS in Y, given in matrix Z. The three matrices Z–Y–X may be envisioned as an “L-shape”; X(I × K) and Z(J × L) share no matrix size dimension, but are connected via Y(I × J ). A few linear combinations (compon...