What is maximum likelihood sequence detection?
Maximum likelihood sequence estimation (MLSE) is a mathematical algorithm to extract useful data out of a noisy data stream.
What is maximum-likelihood sequence estimation equalizer?
Maximum-Likelihood Sequence Estimation (MLSE) equalizers provide optimal equalization of time variations in the propagation channel characteristics. These features output the maximum likelihood sequence estimate of the signal by using an estimate of the channel modeled as a finite input response (FIR) filter.
What is maximum-likelihood criterion?
In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a probability distribution by maximizing a likelihood function, so that under the assumed statistical model the observed data is most probable.
Which is the best definition of maximum likelihood estimation?
Maximum likelihood estimates. Definition. Let X 1, X 2, ⋯, X n be a random sample from a distribution that depends on one or more unknown parameters θ 1, θ 2, ⋯, θ m with probability density (or mass) function f ( x i; θ 1, θ 2, ⋯, θ m). Suppose that ( θ 1, θ 2, ⋯, θ m) is restricted to a given parameter space Ω.
Is the variance of a maximum likelihood Estima-Tor negative?
For large sample sizes, the variance of a maximum likelihood estima- tor of a single parameter is approximately the negative of the reciprocal of the the Fisher information I() = E @2. @. lnL(X) : the negative reciprocal of the second derivative, also known as the curvature, of the log-likelihood function.
Which is the maximum likelihood function in math?
Therefore, the likelihood function L ( p) is, by definition: for 0 < p < 1. Simplifying, by summing up the exponents, we get : Now, in order to implement the method of maximum likelihood, we need to find the p that maximizes the likelihood L ( p).
Which is the maximum likelihood of the normal model?
In summary, we have shown that the maximum likelihood estimators of μ and variance σ 2 for the normal model are: μ ^ = ∑ X i n = X ¯ and σ ^ 2 = ∑ (X i − X ¯) 2 n