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Approximate Bayesian Computation
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics that can be used to estimate the posterior distributions of model parameters. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and ap ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computational Science
Computational science, also known as scientific computing or scientific computation (SC), is a field in mathematics that uses advanced computing capabilities to understand and solve complex problems. It is an area of science that spans many disciplines, but at its core, it involves the development of models and simulations to understand natural systems. * Algorithms ( numerical and non-numerical): mathematical models, computational models, and computer simulations developed to solve science (e.g., biological, physical, and social), engineering, and humanities problems * Computer hardware that develops and optimizes the advanced system hardware, firmware, networking, and data management components needed to solve computationally demanding problems * The computing infrastructure that supports both the science and engineering problem solving and the developmental computer and information science In practical use, it is typically the application of computer simulation and other fo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Quincunx
A quincunx () is a geometric pattern consisting of five points arranged in a cross, with four of them forming a square or rectangle and a fifth at its center. The same pattern has other names, including "in saltire" or "in cross" in heraldry (depending on the orientation of the outer square), the five-point stencil in numerical analysis, and the five dots tattoo. It forms the arrangement of five units in the pattern corresponding to the five-spot on six-sided dice, playing cards, and dominoes. It is represented in Unicode as or (for the die pattern) . Historical origins of the name The quincunx was originally a coin issued by the Roman Republic c. 211–200 BC, whose value was five twelfths (''quinque'' and ''uncia'') of an as, the Roman standard bronze coin. On the Roman quincunx coins, the value was sometimes indicated by a pattern of five dots or pellets. However, these dots were not always arranged in a quincunx pattern. The ''Oxford English Dictionary'' (OED) d ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Normalizing Constant
The concept of a normalizing constant arises in probability theory and a variety of other areas of mathematics. The normalizing constant is used to reduce any probability function to a probability density function with total probability of one. Definition In probability theory, a normalizing constant is a constant by which an everywhere non-negative function must be multiplied so the area under its graph is 1, e.g., to make it a probability density function or a probability mass function. Examples If we start from the simple Gaussian function p(x)=e^, \quad x\in(-\infty,\infty) we have the corresponding Gaussian integral \int_^\infty p(x) \, dx = \int_^\infty e^ \, dx = \sqrt, Now if we use the latter's reciprocal value as a normalizing constant for the former, defining a function \varphi(x) as \varphi(x) = \frac p(x) = \frac e^ so that its integral is unit \int_^\infty \varphi(x) \, dx = \int_^\infty \frac e^ \, dx = 1 then the function \varphi(x) is a probability d ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Marginal Likelihood
A marginal likelihood is a likelihood function that has been integrated over the parameter space. In Bayesian statistics, it represents the probability of generating the observed sample from a prior and is therefore often referred to as model evidence or simply evidence. Concept Given a set of independent identically distributed data points \mathbf=(x_1,\ldots,x_n), where x_i \sim p(x, \theta) according to some probability distribution parameterized by \theta, where \theta itself is a random variable described by a distribution, i.e. \theta \sim p(\theta\mid\alpha), the marginal likelihood in general asks what the probability p(\mathbf\mid\alpha) is, where \theta has been marginalized out (integrated out): :p(\mathbf\mid\alpha) = \int_\theta p(\mathbf\mid\theta) \, p(\theta\mid\alpha)\ \operatorname\!\theta The above definition is phrased in the context of Bayesian statistics in which case p(\theta\mid\alpha) is called prior density and p(\mathbf\mid\theta) is the likelihood. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Probability
Probability is the branch of mathematics concerning numerical descriptions of how likely an Event (probability theory), event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility of the event and 1 indicates certainty."Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory", Alan Stuart and Keith Ord, 6th Ed, (2009), .William Feller, ''An Introduction to Probability Theory and Its Applications'', (Vol 1), 3rd Ed, (1968), Wiley, . The higher the probability of an event, the more likely it is that the event will occur. A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes ("heads" and "tails") are both equally probable; the probability of "heads" equals the probability of "tails"; and since no other outcomes are possible, the probability of either "heads" or "tails" is 1/2 (which could also be written ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Conditional Probability
In probability theory, conditional probability is a measure of the probability of an event occurring, given that another event (by assumption, presumption, assertion or evidence) has already occurred. This particular method relies on event B occurring with some sort of relationship with another event A. In this event, the event B can be analyzed by a conditional probability with respect to A. If the event of interest is and the event is known or assumed to have occurred, "the conditional probability of given ", or "the probability of under the condition ", is usually written as or occasionally . This can also be understood as the fraction of probability B that intersects with A: P(A \mid B) = \frac. For example, the probability that any given person has a cough on any given day may be only 5%. But if we know or assume that the person is sick, then they are much more likely to be coughing. For example, the conditional probability that someone unwell (sick) is coughing might be ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayes' Theorem
In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to an individual of a known age to be assessed more accurately (by conditioning it on their age) than simply assuming that the individual is typical of the population as a whole. One of the many applications of Bayes' theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in the theorem may have different probability interpretations. With Bayesian probability interpretation, the theorem expresses how a degree of belief, expressed as a probability, should rationally change to account for the availability of related evidence. Bayesian inference is fundamental to Bayesia ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Phylogeography
Phylogeography is the study of the historical processes that may be responsible for the past to present geographic distributions of genealogical lineages. This is accomplished by considering the geographic distribution of individuals in light of genetics, particularly population genetics. This term was introduced to describe geographically structured genetic signals within and among species. An explicit focus on a species' biogeography/biogeographical past sets phylogeography apart from classical population genetics and phylogenetics. Past events that can be inferred include population expansion, population bottlenecks, vicariance, dispersal, and migration. Recently developed approaches integrating coalescent theory or the genealogical history of alleles and distributional information can more accurately address the relative roles of these different historical forces in shaping current patterns. Development The term phylogeography was first used by John Avise in his 1987 work '' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Jonathan K
Jonathan may refer to: *Jonathan (name), a masculine given name Media * ''Jonathan'' (1970 film), a German film directed by Hans W. Geißendörfer * ''Jonathan'' (2016 film), a German film directed by Piotr J. Lewandowski * ''Jonathan'' (2018 film), an American film directed by Bill Oliver * ''Jonathan'' (Buffy comic), a 2001 comic book based on the ''Buffy the Vampire Slayer'' television series * ''Jonathan'' (TV show), a Welsh-language television show hosted by ex-rugby player Jonathan Davies People and biblical figures Bible * Jonathan (1 Samuel), son of King Saul of Israel and friend of David, in the Books of Samuel *Jonathan (Judges), in the Book of Judges Judaism *Jonathan Apphus, fifth son of Mattathias and leader of the Hasmonean dynasty of Judea from 161 to 143 BCE * Rabbi Jonathan, 2nd century *Jonathan (High Priest), a High Priest of Israel in the 1st century Other *Jonathan (apple), a variety of apple * "Jonathan" (song), a 2015 song by French singer and songwri ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Most Recent Common Ancestor
In biology and genetic genealogy, the most recent common ancestor (MRCA), also known as the last common ancestor (LCA) or concestor, of a set of organisms is the most recent individual from which all the organisms of the set are descended. The term is also used in reference to the ancestry of groups of genes (haplotypes) rather than organisms. The MRCA of a set of individuals can sometimes be determined by referring to an established pedigree. However, in general, it is impossible to identify the exact MRCA of a large set of individuals, but an estimate of the time at which the MRCA lived can often be given. Such ''time to most recent common ancestor'' (''TMRCA'') estimates can be given based on DNA test results and established mutation rates as practiced in genetic genealogy, or by reference to a non-genetic, mathematical model or computer simulation. In organisms using sexual reproduction, the ''matrilineal MRCA'' and ''patrilineal MRCA'' are the MRCAs of a given population ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Simon Tavaré
Simon Tavaré (born 1952) is the founding Director of the Herbert and Florence Irving Institute of Cancer Dynamics at Columbia University. Prior to joining Columbia, he was Director of the Cancer Research UK Cambridge Institute, Professor of Cancer Research at the Department of Oncology and Professor in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge. Education Tavaré was educated at Oundle School and the University of Sheffield where he was awarded a Bachelor of Science degree in 1974, a Master of Science degree in 1975, and a PhD in 1979. Research and career Tavaré is a computational biologist and statistician, with his research focusing on three main areas: statistical methods for the analysis of next‑generation sequencing data, evolutionary approaches to cancer and methods for the analysis of genomics data. Tavaré's research has been funded by Cancer Research UK, the Royal Society, the European Union, Horizon ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Intractability (complexity)
In theoretical computer science and mathematics, computational complexity theory focuses on classifying computational problems according to their resource usage, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. The theory formalizes this intuition, by introducing mathematical models of computation to study these problems and quantifying their computational complexity, i.e., the amount of resources needed to solve them, such as time and storage. Other measures of complexity are also used, such as the amount of communication (used in communication complexity), the number of logic gate, gates in a circuit (used in circuit complexity) and the number of processors (used in parallel computing). One of the roles of c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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