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- [[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']] We will now consider infinite sequences of random variables. We will discuss what it means for such a sequence to converge. T15 KB (2,578 words) - 12:13, 21 May 2014
- [[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']] ...s, but we will now formalize the concept of random process, including both discrete-time and continuous time.10 KB (1,690 words) - 12:13, 21 May 2014
- [[Category:random variables]] Question 1: Probability and Random Processes3 KB (449 words) - 21:36, 5 August 2018
- <font size="4">Question 1: Probability and Random Processes </font> ...our proof, keep in mind that may be either a discrete or continuous random variable.6 KB (995 words) - 09:21, 15 August 2014
- ...cess. A stochastic process { X(t), t∈T } is an ordered collection of random variables, T where T is the index set and if t is a time in the set, X(t) i ...h models that use X1,…,Xn as independently identically distributed (iid) random variables. However, note that states do not necessarily have to be independ19 KB (3,004 words) - 09:39, 23 April 2014
- ...ven value of θ is denoted by p(x|θ ). It should be noted that the random variable X and the parameter θ can be vector-valued. Now we obtain a set of indepen ...s parameter estimation, the parameter θ is viewed as a random variable or random vector following the distribution p(θ ). Then the probability density func15 KB (2,273 words) - 10:51, 22 January 2015
- ...tinuous random variables and probability mass function in case of discrete random variables and 'θ' is the parameter being estimated. ...variables) or the probability of the probability mass (in case of discrete random variables)'''12 KB (1,986 words) - 10:49, 22 January 2015
- [[Category:random variables]] Question 1: Probability and Random Processes3 KB (538 words) - 00:54, 10 March 2015
- [[Category:random variables]] Question 1: Probability and Random Processes3 KB (454 words) - 10:25, 10 March 2015