Darwinian evolution in games with perfect information

We consider a model of Darwinian evolution in games with perfect information like chess or checkers. The evolution is viewed as a sequence of strategies, each of which wins over its immediate predecessor. We argue that the intelligence level of strategies need not necessarily increase during this type of evolution.     

Aggregate statistical measures and metapopulation dynamics

There are two main types of metapopulation models. Spatially implicit models are analytically tractable but neglect spatial heterogeneities. Spatially explicit models are more realistic but too complex. In this paper, I build a bridge between both approximations. I derive a new metapopulation model using a well-known technique in population genetics. Spatial heterogeneities are captured by an aggregate statistical measure of spatial correlation. When this correlation is zero, i.e., space is homogeneous, the model becomes the well-known Levins' model. As spatial correlation increases, equilibrium patch occupancy decreases from what would be expected under the spatially homogeneous assumption. I proceed by testing how well spatial complexities from a spatially explicit simulation can be encapsulated by such an aggregate statistical measure.     

Some statistical aspects of sampling in morphometry

Morphometry requires, in addition to the accurate counting of points, intersections and transections, meticulous attention to sampling. Methods are described and discussed for ensuring that adequate samples are obtained for a given standard error. For all situations in which stratification is used, the importance of increasing the number of the early strata for reducing the variance is emphasized.     

SCOREM: statistical consolidation of redundant expression measures

Many platforms for genome-wide analysis of gene expression contain ‘redundant’ measures for the same gene. For example, the most highly utilized platforms for gene expression microarrays, Affymetrix GeneChip® arrays, have as many as ten or more probe sets for some genes. Occasionally, individual probe sets for the same gene report different trends in expression across experimental conditions, a situation that must be resolved in order to accurately interpret the data. We developed an algorithm, SCOREM, for determining the level of agreement between such probe sets, utilizing a statistical test of concordance, Kendall''s W coefficient of concordance, and a graph-searching algorithm for the identification of concordant probe sets. We also present methods for consolidating concordant groups into a single value for its corresponding gene and for post hoc analysis of discordant groups. By combining statistical consolidation with sequence analysis, SCOREM possesses the unique ability to identify biologically meaningful discordant behaviors, including differing behaviors in alternate RNA isoforms and tissue-specific patterns of expression. When consolidating concordant behaviors, SCOREM outperforms other methods in detecting both differential expression and overrepresented functional categories.     

Information measures: statistical confidence limits and inference

This paper presents tested statistical methods for reducing sampling bias in, for assigning approximate confidence bounds to, and for testing hypotheses about information-theoretic measures used in the study of animal communication and in behavioral sequence analysis. These procedures are also applicable in fields other than animal behavior, including psychology and ecology. Results of a Monte Carlo evaluation of the methods, including comparison of these techniques with possible alternative procedures, are presented.     

Some statistical issues in microarray gene expression data

In this paper we discuss some of the statistical issues that should be considered when conducting experiments involving microarray gene expression data. We discuss statistical issues related to preprocessing the data as well as the analysis of the data. Analysis of the data is discussed in three contexts: class comparison, class prediction and class discovery. We also review the methods used in two studies that are using microarray gene expression to assess the effect of exposure to radiofrequency (RF) fields on gene expression. Our intent is to provide a guide for radiation researchers when conducting studies involving microarray gene expression data.     

Some statistical considerations in the determination of thresholds in ELISA

The presentation of quantitative results from ELISAs is very variable, and, in particular, the positive-negative threshold or critical value is poorly defined. A simple statistical model is presented which demonstrates how this value may be determined. Depending on what assumptions are made it is possible to estimate the necessary parameters in several different ways. These methods generally form a hierarchy, and greater precision can be achieved by combining variances from several plates. More problematical is the determination of the negative control, and indeed its definition. The results of a series of ELISAs taken from a study of polyphagous predators of the cereal aphid S. avenae are used to illustrate the methods. The ELISAs were done over a period of three months using 86 plates. Recommendations are made for the numbers and levels of controls to be used in future studies.     

Unscheduled DNA synthesis: Some statistical thoughts

Statistical interpretation of results of experiments involving unscheduled DNA synthesis is examined from a design standpoint. Most appropriate methods currently in use are evaluated and some modifications and extensions are suggested. Concerns about replication and/or interaction errors are evaluated and methods for their appropriate handling are discussed. It is suggested that methods incorporating both dose-response and heterogeneity statistics should be considered in treating results from unscheduled DNA synthesis experiments. Proper designs for such experiments are emphasized.Abbreviations ANOVA analysis of variance - MSE mean square error - UDS unscheduled DNA synthesis     

Some statistical properties of the miniature endplate potentials

I will demonstrate that series of miniature endplate potentials (m.e.p.ps) showing a high proportion of so-called giant m.e.p.ps (g.m.e.p.ps) have different statistical structures from series where the proportion of g.m.e.p.ps is low. The nature of the different structures will be discussed on the basis of two statistical models, one for the distribution of the m.e.p.p. amplitudes and one for the series of point events occurring in time.     

Statistical measures for defining an individual's degree of independence within state-dependent dynamic games

Background  

For organisms living or interacting in groups, the decision-making processes of an individual may be based upon aspects of both its own state and the states of other organisms around it. Much research has sought to determine how group decisions are made, and whether some individuals are more likely to influence these decisions than others. State-dependent modelling techniques are a powerful tool for exploring group decision-making processes, but analyses conducted so far have lacked methods for identifying how dependent an individual's actions are on the rest of the group.     

Microarray probe expression measures, data normalization and statistical validation

DNA microarray technology is a high-throughput method for gaining information on gene function. Microarray technology is based on deposition/synthesis, in an ordered manner, on a solid surface, of thousands of EST sequences/genes/oligonucleotides. Due to the high number of generated datapoints, computational tools are essential in microarray data analysis and mining to grasp knowledge from experimental results. In this review, we will focus on some of the methodologies actually available to define gene expression intensity measures, microarray data normalization, and statistical validation of differential expression.     

Practical measures of integrated information for time-series data

A recent measure of 'integrated information', Φ(DM), quantifies the extent to which a system generates more information than the sum of its parts as it transitions between states, possibly reflecting levels of consciousness generated by neural systems. However, Φ(DM) is defined only for discrete Markov systems, which are unusual in biology; as a result, Φ(DM) can rarely be measured in practice. Here, we describe two new measures, Φ(E) and Φ(AR), that overcome these limitations and are easy to apply to time-series data. We use simulations to demonstrate the in-practice applicability of our measures, and to explore their properties. Our results provide new opportunities for examining information integration in real and model systems and carry implications for relations between integrated information, consciousness, and other neurocognitive processes. However, our findings pose challenges for theories that ascribe physical meaning to the measured quantities.     

Some fundamental aspects of information dynamics

Zusammenfassung Mechanisierte Sprachübersetzung, Speichern von Information und Mustererkennung stellen drei Sonderfälle der allgemeinen Operationen Lernen, Ordnen und Erkennen dar. Die Automatisierung solcher Operationen bildet nicht nur ein an und für sich interessantes Problem, sondern eine dringliche Notwendigkeit, sollen die universellen Gro Brechenanlagen nicht an der Komplexität ihrer inneren Organisation (soft-ware) ersticken. Als vielleicht einfachstes dieser drei — im Grunde nie getrennt auftretenden — Vorgänge, wird das Problem der Wiedererkennung betrachtet, d.h. dasjenige der Abbildung eines kontinuierlichen Ensembles reeller, metrischer Objekte auf eine endliche, diskrete Struktur ohne Metrik, wie sie beispielsweise das Innere einer digitalen Rechenanlage darstellt. Der notwendige Übergang vom heute einzig üblichen Speicher zum eigentlichen Gedächtnis hängt unter anderem von der Lösung dieses Problems ab. Seine Untersuchung hebt die grundsätzliche Bedeutung der Umgebung einzelner Objekte — ihres Kontextes — hervor, welche Umgebung im diskreten Raum das notwendige Äquivalent zum Kontinuum darstellt. Dies führt zu einer neuen Betrachtung des Paradoxon von Brillouin, und zur notwendigen Benützung dynamischer Methoden bei der Verarbeitung von Information, anders gesagt bei ihrer Überführung aus der bildhaft-kontinuierlichen Form in die diskrete, und zurück. Womit zumindest eine mögliche Definition nicht-trivialer Informationsverarbeitung gegeben ist.