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.
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
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.
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
Each Mode has a different set of Attributes. The Modes are: , , and .
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
Probability distribution in which all the values in an interval are equally likely to be drawn. It can either be or .
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
Probability distribution of the time between events in a . Its parameter is the rate at which the events in the Poison process occur.
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
Probability distribution in which all the values in an interval are equally likely to be drawn. It can either be or .
| Attribute | Type | Description |
|---|
| Attribute | Type | Description |
|---|
This Mode only uses a uniform distribution. It can either be or .
| Attribute | Type | Description |
|---|
| Input | Type | Description |
|---|
| Output | Type | Description |
|---|
on Wikipedia.
on Wikipedia.
on Wikipedia.
| Bool | Whether the random generator is deterministic or not. |
| Int (only available when | The |
| Drop-down | The probability distribution that the random generator will use. |
| Float (between 0 and 1) | The probability that the outcome will be true. |
| Drop-down | Whether the outcome will be an Int or Byte. |
| Float | The probability that the outcome of each trial is true. |
| Int | The number of independent experiments, each one with probability of success |
| Float | The mean value of the distribution. |
| Float | The standard deviation of the distribution. |
| Drop-down | Wheter the outcome will be an Int or Byte. |
| Float | The mean value of the distribution. |
| Drop-down | Whether an Int, Float, or Byte will be generated. |
| Defined in the | The lower bound of the interval from which the random number will be extracted. |
| Defined in the | The upper bound of the interval from which the random number will be extracted. |
| Drop-down | The type of random generator to use. |
| Int (not available for non_deterministic | The |
| Drop-down | The probability distribution that the random generator will use. |
| Float (between 0 and 1) | The probability that the outcome will be true. |
| Drop-down | Whether the outcome will be an Int or Byte. |
| Float | The probability that the outcome of each trial is true. |
| Int | The number of independent experiments performed, each one with probability of success |
| Float | Defines where the peak is. |
| Float | Half the width of the probability density function at half the maximum height. |
| Float | Number of independent normal random variables that are summed. |
| Float | Rate at which the events in the Poisson process occur. |
| Float | Defines where the peak is. |
| Float | Defines how spread out the values are. |
| Float | Degrees of freedom of the chi-squared random variable in the denominator. |
| Float | Degrees of freedom of the chi-squared random variable in the numerator. |
| Float | Modifies the shape of the probability distribution. |
| Float | Defines how spread out are the values. |
| Drop-down | Whether the output is an Int or Byte. |
| Float (between 0 and 1) | The probability of success in the Bernoulli trials. |
| Float | The mean value of the logarithm of the distribution. |
| Float | The standard deviation of the logarithm of the distribution. |
| Drop-down | Whether the outcome is an Int or Byte. |
| Float (between 0 and 1) | The probability that the outcome of each trial is true. |
| Int | The number of failures to occur until the experiments stop. |
| Float | The mean value of the distribution. |
| Float | The standard deviation of the distribution. |
| Drop-down | Wheter the outcome will be an Int or Byte. |
| Float | The mean value of the distribution. |
| Float | The number of observations taken from a normal distribution minus one. As it grows, the Student-t distribution approaches a normal distribution. |
| Drop-down | Whether an Int, Float, or Byte will be generated. |
| Defined in the | The lower bound of the interval from which the random number will be extracted. |
| Defined in the | The upper bound of the interval from which the random number will be extracted. |
| Float | Defines the shape of the probability distribution. |
| Float | Defines how spread out the values of the probability distribution are. |
| Drop-down | Whether an Int, Float, or Byte will be generated. |
| Defined in the | The lower bound of the interval from which the random number will be extracted. |
| Defined in the | 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. |
| Depends on the | The random outcome that was generated. |
