Concepts inA Markov random field model for term dependencies
Information retrieval
Information retrieval (IR) is the area of study concerned with searching for documents, for information within documents, and for metadata about documents, as well as that of searching structured storage, relational databases, and the World Wide Web. There is overlap in the usage of the terms data retrieval, document retrieval, information retrieval, and text retrieval, but each also has its own body of literature, theory, praxis, and technologies.
more from Wikipedia
Scientific modelling
Scientific modelling is the process of generating abstract, conceptual, graphical or mathematical models. Science offers a growing collection of methods, techniques and theory about all kinds of specialized scientific modelling. A scientific model can provide a way to read elements easily which have been broken down to a simpler form. Modelling is an essential and inseparable part of all scientific activity, and many scientific disciplines have their own ideas about specific types of modelling.
more from Wikipedia
Conceptual model
In the most general sense, a model is anything used in any way to represent anything else. Some models are physical objects, for instance, a toy model which may be assembled, and may even be made to work like the object it represents. They are used to help us know and understand the subject matter they represent. The term conceptual model may be used to refer to models which are represented by concepts or related concepts which are formed after a conceptualization process in the mind.
more from Wikipedia
Markov random field
In the domain of physics and probability, a Markov random field (often abbreviated as MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. A Markov random field is similar to a Bayesian network in its representation of dependencies; the differences being that Bayesian networks are directed and acyclic, whereas Markov networks are undirected and may be cyclic.
more from Wikipedia
Likelihood function
In statistics, a likelihood function (often simply the likelihood) is a function of the parameters of a statistical model, defined as follows: the likelihood of a set of parameter values given some observed outcomes is equal to the probability of those observed outcomes given those parameter values. Likelihood functions play a key role in statistical inference, especially methods of estimating a parameter from a set of statistics.
more from Wikipedia
Mathematical model
A mathematical model is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modelling. Mathematical models are used not only in the natural sciences and engineering disciplines, but also in the social sciences; physicists, engineers, statisticians, operations research analysts and economists use mathematical models most extensively.
more from Wikipedia
Terabyte
The terabyte is a multiple of the unit byte for digital information. The prefix tera means 10 in the International System of Units (SI), and therefore 1 terabyte is 1000000000000bytes, or 1 trillion bytes, or 1000 gigabytes. 1 terabyte in binary prefixes is 0.9095 tebibytes, or 931.32 gibibytes. The unit symbol for the terabyte is TB or TByte, but not Tb (lower case b) which refers to terabit.
more from Wikipedia
Data
Data are values of qualitative or quantitative variables, belonging to a set of items. Data in computing are often represented by a combination of items organized in rows and multiple variables organized in columns. Data are typically the results of measurements and can be visualised using graphs or images. Data as an abstract concept can be viewed as the lowest level of abstraction from which information and then knowledge are derived. Raw data, i.e.
more from Wikipedia