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What does the probability density function tell you?
The area under the pdf between two values is the probability that the random variable lies between those two values.
What are your expectations in mathematics?
In probability theory, the expectation of a discrete random variable X is the sum, calculated over all values that X can take, of : the product of those values and the probability that X takes that value. In the case of a continuous random variable, it is the corresponding integral.
What is the difference between random variable and random process?
A random variable is a variable which can take different values and the values that it takes depends on some probability distribution rather than a deterministic rule. A random process is a process which can be in a number of different states and the transition from one state to another is random.
What is the difference between 'stochastic' and 'deterministic'?
These words are used to describe ways of modeling or understanding the world. "Stochastic" means that some elements of the model or description are thought of as being random. (The word "Stochastic" is derived from an ancient Greek word for random.) A model or description that has no random factors, but conceivably could, is called "deterministic." For example, the equation Q = VC where Q = charge, V = voltage, and C = capacitance, is a deterministic physical model. One stochastic version of it would be Q = VC + e where e is a random variable introduced to account for or characterize the deviations between the actual charges and the values predicted by the deterministic model.
What is marginal probability?
Suppose you have two random variables, X and Y and their joint probability distribution function is f(x, y) over some appropriate domain. Then the marginal probability distribution of X, is the integral or sum of f(x, y) calculated over all possible values of Y.
What is the meaning of random variable in probability distribution?
It is a variable that can take a number of different values. The probability that it takes a value in any given range is determined by a random process and the value of that probability is given by the probability distribution function.It is a variable that can take a number of different values. The probability that it takes a value in any given range is determined by a random process and the value of that probability is given by the probability distribution function.It is a variable that can take a number of different values. The probability that it takes a value in any given range is determined by a random process and the value of that probability is given by the probability distribution function.It is a variable that can take a number of different values. The probability that it takes a value in any given range is determined by a random process and the value of that probability is given by the probability distribution function.
What does the probability density function tell you?
The area under the pdf between two values is the probability that the random variable lies between those two values.
What are your expectations in mathematics?
In probability theory, the expectation of a discrete random variable X is the sum, calculated over all values that X can take, of : the product of those values and the probability that X takes that value. In the case of a continuous random variable, it is the corresponding integral.
Explain probability distribution?
A probability distribution describes the likelihood of different outcomes in a random experiment. It shows the possible values of a random variable along with the probability of each value occurring. Different probability distributions (such as uniform, normal, and binomial) are used to model various types of random events.
What is the difference between random variable and random process?
A random variable is a variable which can take different values and the values that it takes depends on some probability distribution rather than a deterministic rule. A random process is a process which can be in a number of different states and the transition from one state to another is random.
What is discrete probability distribution?
It is a function that gives the probabilities associated with the discrete number of values that a random variable can take.
What does the normal probability density function describe?
A probability density function (pdf) for a continuous random variable (RV), is a function that describes the probability that the RV random variable will fall within a range of values. The probability of the RV falling between two values is the integral of the relevant PDF. The normal or Gaussian distribution is one of the most common distributions in probability theory. Whatever the underlying distribution of a RV, the average of a set of independent observations for that RV will by approximately Gaussian.
What are the 2 conditions that determine a probability distribution?
The value of the distribution for any value of the random variable must be in the range [0, 1]. The sum (or integral) of the probability distribution function over all possible values of the random variable must be 1.
What is probability distibution?
A statistical function that describes all the possible values and likelihoods that a random variable can take within a given range.
Can probability values be negative?
No. Probability values always have to be positive.
How do calculate the probability of at most?
For a discrete variable, you add together the probabilities of all values of the random variable less than or equal to the specified number. For a continuous variable it the integral of the probability distribution function up to the specified value. Often these values may be calculated or tabulated as cumulative probability distributions.
Difference between a random variable and a probability distribution is?
A random variable is a variable that can take different values according to a process, at least part of which is random.For a discrete random variable (RV), a probability distribution is a function that assigns, to each value of the RV, the probability that the RV takes that value.The probability of a continuous RV taking any specificvalue is always 0 and the distribution is a density function such that the probability of the RV taking a value between x and y is the area under the distribution function between x and y.
