Random
Last updated
Last updated
The Random Node generates a random outcome, usually a number.
This Node can be set to three different Modes (Advanced, Expert, and Standard). Each of these Modes offers a different set of Attributes that are explained below.
Each Mode has a different set of Attributes. The Modes are: Advanced, Expert, and Standard.
This Mode allows to choose whether the random generator is deterministic or not, and for the deterministic case, the seed to use.
This Mode has a Drop-down menu from which the probability distribution used for the random generator can be chosen. Each option offers its own set of Attributes with the probability distribution parameters.
Next, the Attributes for each probability distribution are described. For each probability distribution, the link to its corresponding Wikipedia entry is given.
Probability distribution of a random variable that can take two values: true, with probability p; and false, with probability 1-p. When this distribution is chosen, the outcome of the Node is a Boolean.
Probability distribution of the number of successes in a sequence of independent experiments, each one with two possible outcomes: success and failure. The parameters for this probability distribution are the number of experiments and the probability of a successful outcome in each one.
Symmetric probability distribution, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation.
Discrete probability distribution that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value.
Uniform
Probability distribution in which all the values in an interval are equally likely to be drawn. It can either be continuous or discrete.
This Mode allows to choose from a list of several types of random generators.
This Mode has a Drop-down menu from which the probability distribution to be used for the random generator can be chosen. Each option offers its own set of Attributes with the probability distribution parameters.
Next, the Attributes for each probability distribution are described. For each probability distribution, the link to its corresponding Wikipedia entry is given.
Probability distribution of a random variable that can take two values: true, with probability p; and false, with probability 1-p. When this distribution is chosen, the outcome of the Node is a Boolean.
Probability distribution of the number of successes in a sequence of independent experiment, each one with two possible outcomes: success and failure. The parameters for this probability distribution are the number of experiments and the probability of a successful outcome in each one.
Probability distribution that resembles a normal distribution but with a taller peak, whose tails decay slower. Its parameters are the location of the peak and the scale - the latter defines its width.
Probability distribution of a sum of the squares of a number of independent normal random variables. The number of normal random variables is called the degrees of freedom of the Chi-squared distribution.
Probability distribution of the time between events in a Poisson process. Its parameter is the rate at which the events in the Poison process occur.
Limit distribution of properly normalized maxima of a sequence of independent and identically distributed random variables.
Ratio of two independent random variables with chi-squared distributions, each one divided by its corresponding number of degrees of freedom for scaling.
Maximum entropy probability distribution for a random variable, whose mean is the product between the shape and scale, which are the two parameters of the Gamma distribution.
The probability distribution of the number of experiments with a Bernoulli distribution needed to get one success.
Probability distribution of a random variable whose logarithm has a normal distribution.
Probability distribution of the number of successes in a sequence of independent experiments, each with two possible outcomes: success and failure, before a specified non-random number of failures occur. The parameters for this probability distribution are the probability of a successful outcome in each experiment and the number of failures until the experiments stop.
Symmetric probability distribution, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation.
Discrete probability distribution that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value.
Probability distribution that arises when estimating the mean of a normally-distributed statistical population with a small sample size and unknown standard deviation. Its parameter is the number of degrees of freedom, which is the number of observations taken from a normal distribution minus one.
Uniform
Probability distribution in which all the values in an interval are equally likely to be drawn. It can either be continuous or discrete.
This Mode only uses a uniform distribution. It can either be discrete or continuous.
Random number generation on Wikipedia.
Pseudorandom number generator on Wikipedia.
List of probability distributions on Wikipedia.
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Is Deterministic
Bool
Whether the random generator is deterministic or not.
Seed
Int (only available when Is Deterministic is set to true)
The Seed to use for the deterministic random generator.
Distribution
Drop-down
The probability distribution that the random generator will use.
Probability of 'true'
Float (between 0 and 1)
The probability that the outcome will be true.
Data Type
Drop-down
Whether the outcome will be an Int or Byte.
Probability of 'true'
Float
The probability that the outcome of each trial is true.
Number of trials
Int
The number of independent experiments, each one with probability of success Probability of 'true'.
Mean
Float
The mean value of the distribution.
Standard deviation
Float
The standard deviation of the distribution.
Data Type
Drop-down
Wheter the outcome will be an Int or Byte.
Mean
Float
The mean value of the distribution.
Data Type
Drop-down
Whether an Int, Float, or Byte will be generated.
Minimum
Defined in the Data Type Attribute
The lower bound of the interval from which the random number will be extracted.
Maximum
Defined in the Data Type Attribute
The upper bound of the interval from which the random number will be extracted.
Generator
Drop-down
The type of random generator to use.
Seed
Int (not available for non_deterministic Generator)
The Seed to use for the random generator.
Distribution
Drop-down
The probability distribution that the random generator will use.
Probability of 'true'
Float (between 0 and 1)
The probability that the outcome will be true.
Data Type
Drop-down
Whether the outcome will be an Int or Byte.
Probability of 'true'
Float
The probability that the outcome of each trial is true.
Number of trials
Int
The number of independent experiments performed, each one with probability of success Probability of 'true'.
Location
Float
Defines where the peak is.
Scale
Float
Half the width of the probability density function at half the maximum height.
Degrees of freedom
Float
Number of independent normal random variables that are summed.
Rate
Float
Rate at which the events in the Poisson process occur.
Location
Float
Defines where the peak is.
Scale
Float
Defines how spread out the values are.
Denominator Dof
Float
Degrees of freedom of the chi-squared random variable in the denominator.
Numerator DoF
Float
Degrees of freedom of the chi-squared random variable in the numerator.
Shape
Float
Modifies the shape of the probability distribution.
Scale
Float
Defines how spread out are the values.
Data Type
Drop-down
Whether the output is an Int or Byte.
Probability of 'true'
Float (between 0 and 1)
The probability of success in the Bernoulli trials.
Mean
Float
The mean value of the logarithm of the distribution.
Standard deviation
Float
The standard deviation of the logarithm of the distribution.
Data Type
Drop-down
Whether the outcome is an Int or Byte.
Probability of 'true'
Float (between 0 and 1)
The probability that the outcome of each trial is true.
Number of trials
Int
The number of failures to occur until the experiments stop.
Mean
Float
The mean value of the distribution.
Standard deviation
Float
The standard deviation of the distribution.
Data Type
Drop-down
Wheter the outcome will be an Int or Byte.
Mean
Float
The mean value of the distribution.
Degrees of freedom
Float
The number of observations taken from a normal distribution minus one. As it grows, the Student-t distribution approaches a normal distribution.
Data Type
Drop-down
Whether an Int, Float, or Byte will be generated.
Minimum
Defined in the Data Type Attribute
The lower bound of the interval from which the random number will be extracted.
Maximum
Defined in the Data Type Attribute
The upper bound of the interval from which the random number will be extracted.
Shape
Float
Defines the shape of the probability distribution.
Scale
Float
Defines how spread out the values of the probability distribution are.
Data Type
Drop-down
Whether an Int, Float, or Byte will be generated.
Minimum
Defined in the Data Type Attribute
The lower bound of the interval from which the random number will be extracted.
Maximum
Defined in the Data Type Attribute
The upper bound of the interval from which the random number will be extracted.
Pulse Input (►)
Pulse
A standard Input Pulse, to trigger the execution of the Node.
Pulse Output (►)
Pulse
A standard Output Pulse, to move onto the next Node along the Logic Branch, once this Node has finished its execution.
Output
Depends on the Mode and Distribution
The random outcome that was generated.
