This article summarizes and shares what is a model, the classification of the model, the methods and steps of building a model, and specific cases. In the previous bulk sms service article "Top 10 Basic Skills for Product Managers (1): Reading Through SQL", the last article in the series of articles on the top 10 basic skills of product managers, the author mentioned the overview of models. This article focuses on how product managers understand models? What knowledge points should product managers know in the process of team building models? Then use the case of mask demand to show the process and ideas of building the model. 1. Definition of the model Models and algorithms refer to the same thing in a way, just like in bulk sms service everyday life you ask which came first, the chicken or the egg. A model is a description of a system using mathematical concepts and language.
The process of creating a model is called modeling. 2. Classification of models A model usually consists of relationships and variables. Relations can be described by bulk sms service operators, such as algebraic operators, functions, differential operators, etc. A variable is an abstract form of a quantifiable system parameter of interest. Operators can be combined with variables to function, or not combined with variables. Typically, models can be divided into the following categories: Linear and nonlinear: In a model, if all variables exhibit a linear relationship, the resulting model is linear. Otherwise, it is a nonlinear model. Static bulk sms service and Dynamic: Dynamic models work on how the state of the system changes over time, while static (or steady-state) models are computed while the system remains stationary and therefore time independent.
Dynamic models are usually described by differential equations. Explicit vs. Implicit: If all the input parameters of the overall model are known, and the output parameters bulk sms service can be obtained by a finite number of computations (called linear programming, not to be confused with the linear models described above), the model is called explicit Model. Discrete and continuous: Discrete models treat objects as discrete, such as particles in molecular models, or states in probabilistic models. A continuous model, on the other hand, is described by continuous objects, such as the velocity field of a fluid in a pipe, the temperature and pressure bulk sms service in a solid, a point charge in an electric field that continuously acts on the entire model, and so on. Deterministic vs Probabilistic (stochastic): A deterministic model is one in which the state of all sets of variables