A hidden variable or a latent variable is
a variable in a belief network whose value is not observed for any of the examples
. That is, there is no column in the data corresponding to that variable. Model. Data.
What is variable in artificial intelligence?
Variables can be
primitive
and a possible world corresponds to a total assignment of a value to each variable. Worlds can be primitive and a variable is a function from possible worlds into the domain of the variable; given a possible world, the function returns the value of that variable in that possible world.
Hidden variables may refer to:
Confounding
, in statistics, an extraneous variable in a statistical model that correlates (directly or inversely) with both the dependent variable and the independent variable.
The designation of variables as underlying “hidden” variables
depends on the level of physical description
(so, for example, “if a gas is described in terms of temperature, pressure, and volume, then the velocities of the individual atoms in the gas would be hidden variables”).
Learning with hidden variables – the
EM algorithm
It is a very general algorithm used to learn probabilistic models in which variables are hidden; that is, some of the variables are not observed. Models with hidden variables are sometimes called latent variable models.
Hidden Variables. A public health student
gathers data on bottled water use in her state
. Her data indicate that households that use bottled water have healthier children than households that don’t. She concludes that drinking bottled water instead of tap water helps to prevent childhood diseases.
Hidden variables are
extra variables added to the model of an experiment to explain correlations in
.
the outcomes
.
What is the target variable?
The target variable of a dataset is
the feature of a dataset about which you want to gain a deeper understanding
. A supervised machine learning algorithm uses historical data to learn patterns and uncover relationships between other features of your dataset and the target.
Is Siri narrow AI?
Every sort of machine intelligence that surrounds us today
is Narrow AI
. Google Assistant, Google Translate, Siri and other natural language processing tools are examples of Narrow AI. … They lack the self-awareness, consciousness, and genuine intelligence to match human intelligence.
What are sub fields of AI?
Major sub-fields of AI now include:
Machine Learning, Neural Networks, Evolutionary Computation, Vision, Robotics, Expert Systems, Speech Processing, Natural Language Processing, and Planning
.
Quality of life is a
latent variable
which cannot be measured directly so observable variables are used to infer quality of life. Observable variables to measure quality of life include wealth, employment, environment, physical and mental health, education, recreation and leisure time, and social belonging.
What is a mediating variable in research?
Subject Index Entry. In communication research, a mediating variable is
a variable that links the independent and the dependent variables, and whose existence explains the relationship between the other two variables
. A mediating variable is also known as a mediator variable or an intervening variable.
How one variable affects another happens in what?
The idea is that one variable is the effect of another variable or, to say it another way, that one variable precedes and/or causes
another
. The dependent variable is the variable to be explained (the ‘effect”). The independent variable is the variable expected to account for (the “cause” of) the dependent variable.
What is the goal of machine learning?
The purpose of machine learning is
to discover patterns in your data and then make predictions based on often complex patterns to answer business questions
, detect and analyse trends and help solve problems.
What is EM algorithm used for?
The EM algorithm is used to
find (local) maximum likelihood parameters of a statistical model
in cases where the equations cannot be solved directly. Typically these models involve latent variables in addition to unknown parameters and known data observations.
What is EM algorithm in machine learning?
The expectation-maximization algorithm is
an approach for performing maximum likelihood estimation in the presence of latent variables
. It does this by first estimating the values for the latent variables, then optimizing the model, then repeating these two steps until convergence.
