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A comparison of random forest variable selection methods for regression modeling of continuous outcomes. [PDF]
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2011 IEEE 26th Annual Symposium on Logic in Computer Science, 2011
We introduce the domain of continuous random variables (CRV) over a domain, as an alternative to Jones and Plotkin's probabilistic power domain. While no known Cartesian-closed category is stable under the latter, we show that the so-called thin (uniform) CRVs define a strong monad on the Cartesian-closed category of bc-domains.
Goubault-Larrecq, Jean, Varacca, Daniele
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We introduce the domain of continuous random variables (CRV) over a domain, as an alternative to Jones and Plotkin's probabilistic power domain. While no known Cartesian-closed category is stable under the latter, we show that the so-called thin (uniform) CRVs define a strong monad on the Cartesian-closed category of bc-domains.
Goubault-Larrecq, Jean, Varacca, Daniele
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A review of multiaxial fatigue criteria for random variable amplitude loads
, 2017Nowadays, the estimation of fatigue life under multiaxial random loading is still an extremely complex task. In this paper, a comprehensive review of the multiaxial random fatigue criteria available in the literature is presented. Such a review is mainly
A. Carpinteri, A. Spagnoli, S. Vantadori
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Convolution of generated random variable from exponential distribution with stabilizer constant
, 2015The new distribution is constructed by generating random variables from an exponential distribution with stabilizer constant. It is presented the convolution from this identically and independent new random variable by using analytical methods in the ...
D. Devianto +3 more
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A Test of Missing Completely at Random for Multivariate Data with Missing Values
, 1988A common concern when faced with multivariate data with missing values is whether the missing data are missing completely at random (MCAR); that is, whether missingness depends on the variables in the data set. One way of assessing this is to compare the
R. Little
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1993
Publisher Summary This chapter focuses on the random variables of the probability theory. The random variables of interest take on either a finite or a countable number of possible values. Such random variables are called discrete. However, there also exist random variables that take on a continuum of possible values.
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Publisher Summary This chapter focuses on the random variables of the probability theory. The random variables of interest take on either a finite or a countable number of possible values. Such random variables are called discrete. However, there also exist random variables that take on a continuum of possible values.
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Random Variables and Their Distribution
2013The purpose of performing experiments and collecting data is to gain information on certain quantities of interest called random variables. The exact value of these quantities cannot be known with absolute precision, but rather we can constrain the variable to a given range of values, narrower or wider according to the nature of the variable itself and
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1999
In this chapter, we present practical techniques that can produce random variables from both standard and nonstandard distributions by using a computer program. Given the availability of a uniform generator in R, as explained in Section 2.1.1, we do not deal with the specific production of uniform random variables.
Christian P. Robert, George Casella
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In this chapter, we present practical techniques that can produce random variables from both standard and nonstandard distributions by using a computer program. Given the availability of a uniform generator in R, as explained in Section 2.1.1, we do not deal with the specific production of uniform random variables.
Christian P. Robert, George Casella
openaire +2 more sources