Model classification models

2012-01-18T17:42:00.000-08:00

Some models are replicas of the physical properties (relative shape, form, and weight) of the object they represent. Others are physical models but do not have the same physical appearance as the object of their representation. A third type of model deals with symbols and numerical relationships and expressions. Each of these fits within an overall classification of four main categories: physical models, schematic models, verbal models, and mathematical models.

abstract model

Modeling is one of the most powerful toolsavailable for understanding, documenting, and managing the complexity of theinfrastructures required to operate the energy system of the future. It is farless expensive to construct a model to test theories or techniques than toconstruct an actual entity only to find out that one crucial technique is wrongand the entire entity must be re-constructed.
Models have been used extensively by manyindustries as the basis to analyze and design complex systems. Thetelecommunications industries have made extensive use of modeling to developthe diverse communications infrastructure(s) in widespread use today. Physicalmodels are used in many industries, ranging from airplanes and Mars Landers tocircuit breakers and transformers. Building architects use paper models(blueprints) to capture all the complexity in a modern high-rise building.Virtual models are increasingly being used to model even more complex concepts,from weather patterns to cosmology and, of particular interest to IntelliGrid Architectureproject, to information management. One can even make a simple abstract modelof the IntelliGrid Architecture, see Figure 9 below.
simulation model

A simulation model is a mathematical model of a system or process that includes key inputs which affect it and the corresponding outputs that are affected by it. If the model explicitly includes uncertainty, we refer to it as a Monte Carlo simulation model. For example, it can calculate the impact of uncertain inputs and decisions we make on outcomes that we care about, such as profit and loss, investment returns, environmental consequences, and the like. Such a model can be created by writing code in a programming language, statements in a simulation modeling language, or formulas in a Microsoft Excel spreadsheet. Regardless of how it is expressed, a simulation model will include:
- Model inputs that are uncertain numbers -- we'll call these uncertain variables
- Intermediate calculations as required
- Model outputs that depend on the inputs -- we'll call these uncertain functions
It's essential to realize that model outputs that depend on uncertain inputs are uncertain themselves -- hence we talk about uncertain variables and uncertain functions. When we perform a simulation with this model, we will test many different numeric values for the uncertain variables, and we'll obtain many different numeric values for the uncertain functions. We'll use statistics to analyze and summarize all the values for the uncertain functions (and, if we wish, the uncertain variables).
heterogenous model

The homogeneity hypothesis implies that the substitution process ultimately reaches an equilibrium and it is also assumed that the process was already stationary at the very beginning, i.e.,at the root of the phylogeny. If the homogeneity and stationarity assumptions were true, equal nucleotide frequencies would be expected in past and present-day sequences. Actually, we can observe discrepancy's in nucleotide frequencies in many real data sets of present species: model assumptions are clearly violated when using real sequences.

It has been noticed that sequences of similar composition tend to be grouped together irrespective of their real phylogenetic relationships (Lockhart et al., 1994; Tarrio et al., 2001, see e.g.,). In an attempt to avoid this bias, we developed an HETEROGENEOUS model in a Bayesian framework which models coarsely the heterogeneity using a small pool of homogeneous processes. Each branch of the tree ``chooses'' a substitution model among them. The likelihood computation now depends on the position of the root which is why a heterogeneous rooted tree was implemented in PHASE(ultrametricity is optional). The composition observed at this root becomes a free parameter of the model (see, e.g., Yang and Roberts, 1995; Galtier and Gouy, 1998).

Algorithms developed in PHASEare very similar to those implemented in P4by Peter Foster. PHASEframework might be a bit more general since the full substitution model is allowed vary over the tree. However, you are strongly advised to limit yourself to variation of the composition vector as Foster (2004) did. Unfortunately, we did not have time to implement a way to use a single exchangeability matrix over the whole tree yet. There is a workaround (a bit unsatisfactory): you can start the MCMC chain from a model properly initialized and turn off the perturbation of rate ratios so that they have constant values. Use the same trick to fix the gamma shape parameter and the proportion of invariant sites to a single constant value. (Do not use a +I model with HETEROGENEOUS without constraining the proportion of invariant sites to a constant value).

PHASEis missing an efficient MCMC proposal to modify the position of the root. You are advised to use an outgroup in the TREE block to constrain the position of the common ancestor. Remember that this outgroup can also be a monophyletic cluster.
model building methodology

System performance evaluation techniques are of vital importance in the system design process. As depicted schematically in Figure 1, the selection of the design variables is generally accomplished by an iterative process in which the evaluation of the system cost and performance plays a crucial part.

Conceptual model

2012-01-18T17:07:00.000-08:00

Entry:	conceptual model

Definition:	a type of diagram which shows of a set of relationships between factors that are believed to impact or lead to a target condition; a diagram that defines theoretical entities, objects, or conditions of a system and the relationships between them
Example:	This conceptual model has been divided into a set of process flow models, data flows, a logical data mdodel, and a data dictionary.

Cs-elctive

Model classification models

Conceptual model