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Birth–death Process
The birth–death process (or birth-and-death process) is a special case of continuous-time Markov process where the state transitions are of only two types: "births", which increase the state variable by one and "deaths", which decrease the state by one. The model's name comes from a common application, the use of such models to represent the current size of a population where the transitions are literal births and deaths. Birth–death processes have many applications in demography, queueing theory, performance engineering, epidemiology, biology and other areas. They may be used, for example, to study the evolution of bacteria, the number of people with a disease within a population, or the number of customers in line at the supermarket. When a birth occurs, the process goes from state ''n'' to ''n'' + 1. When a death occurs, the process goes from state ''n'' to state ''n'' − 1. The process is specified by birth rates \_ and death rates \_. Re ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Continuous-time Markov Process
A continuous-time Markov chain (CTMC) is a continuous stochastic process in which, for each state, the process will change state according to an exponential random variable and then move to a different state as specified by the probabilities of a stochastic matrix. An equivalent formulation describes the process as changing state according to the least value of a set of exponential random variables, one for each possible state it can move to, with the parameters determined by the current state. An example of a CTMC with three states \ is as follows: the process makes a transition after the amount of time specified by the holding time—an exponential random variable E_i, where ''i'' is its current state. Each random variable is independent and such that E_0\sim \text(6), E_1\sim \text(12) and E_2\sim \text(18). When a transition is to be made, the process moves according to the jump chain, a discrete-time Markov chain with stochastic matrix: :\begin 0 & \frac & \frac \\ \frac & ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Differential Equations
In mathematics, a differential equation is an equation that relates one or more unknown functions and their derivatives. In applications, the functions generally represent physical quantities, the derivatives represent their rates of change, and the differential equation defines a relationship between the two. Such relations are common; therefore, differential equations play a prominent role in many disciplines including engineering, physics, economics, and biology. Mainly the study of differential equations consists of the study of their solutions (the set of functions that satisfy each equation), and of the properties of their solutions. Only the simplest differential equations are solvable by explicit formulas; however, many properties of solutions of a given differential equation may be determined without computing them exactly. Often when a closed-form expression for the solutions is not available, solutions may be approximated numerically using computers. The theory of d ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Exponential Distribution
In probability theory and statistics, the exponential distribution is the probability distribution of the time between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate. It is a particular case of the gamma distribution. It is the continuous analogue of the geometric distribution, and it has the key property of being memoryless. In addition to being used for the analysis of Poisson point processes it is found in various other contexts. The exponential distribution is not the same as the class of exponential families of distributions. This is a large class of probability distributions that includes the exponential distribution as one of its members, but also includes many other distributions, like the normal, binomial, gamma, and Poisson distributions. Definitions Probability density function The probability density function (pdf) of an exponential distribution is : f(x;\lambda) = \begin \lam ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Poisson Process
In probability, statistics and related fields, a Poisson point process is a type of random mathematical object that consists of points randomly located on a mathematical space with the essential feature that the points occur independently of one another. The Poisson point process is often called simply the Poisson process, but it is also called a Poisson random measure, Poisson random point field or Poisson point field. This point process has convenient mathematical properties, which has led to its being frequently defined in Euclidean space and used as a mathematical model for seemingly random processes in numerous disciplines such as astronomy,G. J. Babu and E. D. Feigelson. Spatial point processes in astronomy. ''Journal of statistical planning and inference'', 50(3):311–326, 1996. biology,H. G. Othmer, S. R. Dunbar, and W. Alt. Models of dispersal in biological systems. ''Journal of mathematical biology'', 26(3):263–298, 1988. ecology,H. Thompson. Spatial point processes, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kendall's Notation
In queueing theory, a discipline within the mathematical theory of probability, Kendall's notation (or sometimes Kendall notation) is the standard system used to describe and classify a queueing node. D. G. Kendall proposed describing queueing models using three factors written A/S/''c'' in 1953 where A denotes the time between arrivals to the queue, S the service time distribution and ''c'' the number of service channels open at the node. It has since been extended to A/S/''c''/''K''/''N''/D where ''K'' is the capacity of the queue, ''N'' is the size of the population of jobs to be served, and D is the queueing discipline. When the final three parameters are not specified (e.g. M/M/1 queue), it is assumed ''K'' = ∞, ''N'' = ∞ and D = FIFO. A: The arrival process A code describing the arrival process. The codes used are: S: The service time distribution This gives the distribution of time of the service of a customer. Some common notations are ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Queueing Model
Queueing theory is the mathematical study of waiting lines, or queues. A queueing model is constructed so that queue lengths and waiting time can be predicted. Queueing theory is generally considered a branch of operations research because the results are often used when making business decisions about the resources needed to provide a service. Queueing theory has its origins in research by Agner Krarup Erlang when he created models to describe the system of Copenhagen Telephone Exchange company, a Danish company. The ideas have since seen applications including telecommunication, traffic engineering, computing and, particularly in industrial engineering, in the design of factories, shops, offices and hospitals, as well as in project management. Spelling The spelling "queueing" over "queuing" is typically encountered in the academic research field. In fact, one of the flagship journals of the field is '' Queueing Systems''. Single queueing nodes A queue, or queueing nod ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Phylogenies
A phylogenetic tree (also phylogeny or evolutionary tree Felsenstein J. (2004). ''Inferring Phylogenies'' Sinauer Associates: Sunderland, MA.) is a branching diagram or a tree showing the evolutionary relationships among various biological species or other entities based upon similarities and differences in their physical or genetic characteristics. All life on Earth is part of a single phylogenetic tree, indicating common ancestry. In a ''rooted'' phylogenetic tree, each node with descendants represents the inferred most recent common ancestor of those descendants, and the edge lengths in some trees may be interpreted as time estimates. Each node is called a taxonomic unit. Internal nodes are generally called hypothetical taxonomic units, as they cannot be directly observed. Trees are useful in fields of biology such as bioinformatics, systematics, and phylogenetics. ''Unrooted'' trees illustrate only the relatedness of the leaf nodes and do not require the ancestral root to be ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Phylodynamics
Viral phylodynamics is defined as the study of how epidemiological, immunological, and evolutionary processes act and potentially interact to shape viral phylogenies. Since the coining of the term in 2004, research on viral phylodynamics has focused on transmission dynamics in an effort to shed light on how these dynamics impact viral genetic variation. Transmission dynamics can be considered at the level of cells within an infected host, individual hosts within a population, or entire populations of hosts. Many viruses, especially RNA viruses, rapidly accumulate genetic variation because of short generation times and high mutation rates. Patterns of viral genetic variation are therefore heavily influenced by how quickly transmission occurs and by which entities transmit to one another. Patterns of viral genetic variation will also be affected by selection acting on viral phenotypes. Although viruses can differ with respect to many phenotypes, phylodynamic studies have to date tende ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
M/M/c Model
In queueing theory, a discipline within , the queue (or Erlang–T model) is a multi-server queueing model. In Kendall's notation it describes a system where arrivals form a single queue and are governed by a , there are servers, and job service times are exponentially distributed. It is a generalization of which considers only a single server. The model with infinitely many servers is the M/M/∞ queue. Model definition An M/M/c queue is a stochastic process whose state space is the set where the value corresponds to the number of customers in the system, including any currently in service. * Arrivals occur at rate according to a Poisson process and move the process from state to +1. * Service times have an exponential distribution with parameter . If there are fewer than jobs, some of the servers will be idle. If there are more than jobs, the jobs queue in a buffer. * The buffer is of infinite size, so there is no limit on the number of customers it can contain. The mod ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
M/M/1 Model
In queueing theory, a discipline within the mathematical theory of probability, an M/M/1 queue represents the queue length in a system having a single server, where arrivals are determined by a Poisson process and job service times have an exponential distribution. The model name is written in Kendall's notation. The model is the most elementary of queueing models and an attractive object of study as closed-form expressions can be obtained for many metrics of interest in this model. An extension of this model with more than one server is the M/M/c queue. Model definition An M/M/1 queue is a stochastic process whose state space is the set where the value corresponds to the number of customers in the system, including any currently in service. * Arrivals occur at rate λ according to a Poisson process and move the process from state ''i'' to ''i'' + 1. * Service times have an exponential distribution with rate parameter μ in the M/M/1 queue, where 1/μ is the mean ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pure Birth Process
In probability theory, a birth process or a pure birth process is a special case of a continuous-time Markov process and a generalisation of a Poisson process. It defines a continuous process which takes values in the natural numbers and can only increase by one (a "birth") or remain unchanged. This is a type of birth–death process with no deaths. The rate at which births occur is given by an exponential random variable whose parameter depends only on the current value of the process Definition Birth rates definition A birth process with birth rates (\lambda_n, n\in \mathbb) and initial value k\in \mathbb is a minimal right-continuous process (X_t, t\ge 0) such that X_0=k and the interarrival times T_i = \inf\ - \inf\ are independent exponential random variables with parameter \lambda_i. Infinitesimal definition A birth process with rates (\lambda_n, n\in \mathbb) and initial value k\in \mathbb is a process (X_t, t\ge 0) such that: * X_0=k * \forall s,t\ge 0: s [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Population Size
In population genetics and population ecology, population size (usually denoted ''N'') is the number of individual organisms in a population. Population size is directly associated with amount of genetic drift, and is the underlying cause of effects like population bottlenecks and the founder effect. Genetic drift is the major source of decrease of genetic diversity within populations which drives fixation and can potentially lead to speciation events. Genetic drift Of the five conditions required to maintain Hardy-Weinberg Equilibrium, infinite population size will always be violated; this means that some degree of genetic drift is always occurring. Smaller population size leads to increased genetic drift, it has been hypothesized that this gives these groups an evolutionary advantage for acquisition of genome complexity. An alternate hypothesis posits that while genetic drift plays a larger role in small populations developing complexity, selection is the mechanism by which la ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
