|
| |
|
|
Inffering the demographic history of populations from genomic data
Simandlová, Sára ; Reifová, Radka (advisor) ; Černá Bolfíková, Barbora (referee)
Currently, it is not difficult to obtain genomic data even from non-model organisms. These data can give us information about the demographic history of populations. Many statistical inference procedures have been developed to infer the demographic history of populations from genomic data, and I describe them in this thesis. In the introduction, I introduce the reader to important concepts in the analysis of the demographic history of populations. I then describe the different types of genomic data that can be used to infer the demographic history of populations. I then present the flow of an experiment in inferring the demographic history of populations, where I elaborate on the steps and present the approaches and methods that are used today. I provide a basic overview of the theory and logic behind each approach. I also introduce the reader the most commonly used software packages for inferring demographic histories of populations and provide a comparison between them.
|
|
|
Inferring the demographic history of populations from genomic data
Simandlová, Sára ; Reifová, Radka (advisor) ; Černá Bolfíková, Barbora (referee)
Currently, it is not difficult to obtain genomic data even from non-model organisms. These data can give us information about the demographic history of populations. Many statistical inference methods have been developed to infer the demographic history of populations from genomic data, which I describe in this bachelor thesis. At first, I introduce the reader to important concepts in analyzing the demographic history of populations. I then describe the different types of genomic data that can be used to infer the demographic history of populations. Next, I discuss statistical methods, which include methods based on site frequency spectrum data, methods using approximate Bayesian computation, methods for determining identity, and sequential Markov coalescent methods. I provide a basic overview of the theory and logic of each approach. I then present procedures for selecting inference methods. Keywords: population genetics, demographic inference, statistical inference, whole genome data
|
|
|
Exponenciální třídy a jejich význam pro statistickou inferenci
Moneer Borham Abdel-Maksoud, Sally ; Hlubinka, Daniel (advisor) ; Antoch, Jaromír (referee)
Title: Exponential families in statistical inference Author: Sally Abdel-Maksoud Department: Department of Probability and Mathematical Statistics Supervisor: doc. RNDr. Daniel Hlubinka, Ph.D. Supervisor's e-mail address: Daniel.Hlubinka@mff.cuni.cz Abstract: This diploma thesis provides an evaluation of Exponential families of distributions which has a special position in mathematical statistic including appropriate properties for estimation of population parameters, hypothesis testing and other inference problems. Diploma will introduce the basic concepts and facts associated with the distribution of exponential type especially with focusing on the advantages of exponential families in classical parametric statistics, thus in theory of estimation and hypothesis testing. Emphasis will be placed on one-parameter and multi- parameters systems. It also exposes an important concepts about the curvature of a statistical problem including the curvature in exponential families. We will define a quantity that measure how nearly "exponential" the families are. This quantity is said to be the statistical curvature of the family. We will show that the family with a small curvature enjoy the good properties of exponential families Moreover, the properties of the curvature, hypotheses testing and some...
|
|
| |
|
|
Analýza dat týkajících se risku sebevraždy u mentálně nemocných
Hron, Jiří ; Rauch, Jan (advisor) ; Malá, Ivana (referee)
The three goals of this thesis are to present a coherent overview of the research on suicide in both the general population and among mentally ill, to analyse records of hospitalisations of mentally ill from years 2006 to 2012 while looking for patterns either leading to identification of suicide risk factors or useful for predicting probability of suicide at the time of discharge, and finally to compare a selected subset of statistical, data mining and machine learning methods in relation to their applicability to the second goal. The overview is based on information from over 40 published articles. The analysis and the comparison make use of associative rules mining, visual and stepwise methods for exploration, standard and conditional logistic regression models for inference, and variations of random forests for prediction. To the best of author's knowledge, none of the three goals was previously pursued by any other researcher in the Czech Republic, certainly not using the data set provided for purposes of this thesis. A new modification of random forest combined with a set of logistic regression in order to refine prediction accuracy is also briefly explored. The structure closely follows the above--stated goals starting from the chapters on related work and on the theoretical basis of the methods used, and concluding by the analysis itself and discussion of its results.
|
guest ::
