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Related Concept Videos

Probability in Statistics01:14

Probability in Statistics

Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
Probability Distributions01:32

Probability Distributions

The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Binomial Probability Distribution01:15

Binomial Probability Distribution

A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
Probability Histograms01:17

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
Introduction to Statistics01:17

Introduction to Statistics

The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...

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Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

Probability, statistics, and computational science.

Niko Beerenwinkel1, Juliane Siebourg

  • 1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. niko.beerenwinkel@bsse.ethz.ch

Methods in Molecular Biology (Clifton, N.J.)
|March 13, 2012
PubMed
Summary
This summary is machine-generated.

This chapter introduces fundamental probability and computational statistics for evolutionary genomics. It covers statistical modeling, maximum likelihood, Bayesian inference, and common models like Markov chains for genomic data analysis.

Related Experiment Videos

Last Updated: May 24, 2026

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

Area of Science:

  • Evolutionary Genomics
  • Computational Statistics
  • Probability Theory

Background:

  • Evolutionary genomics relies heavily on statistical and computational methods.
  • Understanding foundational concepts is crucial for advanced genomic analyses.

Purpose of the Study:

  • To provide a foundational review of probability theory and computational statistics relevant to evolutionary genomics.
  • To introduce statistical modeling principles, including maximum likelihood and Bayesian inference.
  • To detail Markov chains, hidden Markov models, and Bayesian networks for genomics applications.

Main Methods:

  • Review of basic probability and statistical concepts.
  • Introduction to statistical modeling principles (maximum likelihood, Bayesian inference).
  • Detailed explanation of Markov chains, hidden Markov models, and Bayesian networks.
  • Discussion of inference algorithms and learning from partially observed data.

Main Results:

  • Provides a comprehensive overview of essential statistical concepts for evolutionary genomics.
  • Explains the application and inference of various probabilistic models in genomic research.
  • Offers examples illustrating the practical use of these models.

Conclusions:

  • A strong grasp of these statistical foundations is essential for advancing evolutionary genomics research.
  • The chapter serves as a prerequisite for understanding more complex models and algorithms in subsequent chapters.
  • Efficient inference and model learning from data are key challenges addressed.