Cramér–Rao lower bound (CRLB) — scalar parameter formula & Matlab check
Cramér–Rao lower bound (CRLB) for scalar estimation: Fisher information formula, DC-in-AWGN example, and Matlab Monte Carlo vs the bound.
Signal Processing for Communication Systems
Cramér–Rao lower bound (CRLB) for scalar estimation: Fisher information formula, DC-in-AWGN example, and Matlab Monte Carlo vs the bound.
Key concept: Cramér-Rao bound is the lower bound on variance of unbiased estimators that estimate deterministic parameters. Introduction The criteria for existence of having an Minimum Variance Unbiased Estimator (MVUE) was discussed in a previous article. To have an MVUE, it is necessary to have estimates that are unbiased and that give minimum variance (compared … Read more
Score function and Fisher information: how estimator sensitivity is measured, the link to CRLB, and how to compute it for common observation models.
As a pre-requisite, check out the previous article on the logic behind deriving the maximum likelihood estimator for a given PDF. Let X=(x1,x2,…, xN) are the samples taken from Gaussian distribution given by Calculating the Likelihood The log likelihood is given by, Differentiating and equating to zero to find the maxim (otherwise equating the score … Read more
As a pre-requisite, check out the previous article on the logic behind deriving the maximum likelihood estimator for a given PDF. Let X=(x1,x2,…, xN) are the samples taken from Exponential distribution given by $latex f(x;\theta) = \theta e^{-\theta x } $ Calculating the Likelihood The log likelihood is given by, Differentiating and equating to zero … Read more
Suppose X=(x1,x2,…, xN) are the samples taken from a random distribution whose PDF is parameterized by the parameter $latex \theta $. If the PDF of the underlying parameter satisfies some regularity condition (if the log of the PDF is differentiable) then the likelihood function is given by Here $latex f_N(x_N;\theta) $ is the PDF of … Read more
Key focus: Understand maximum likelihood estimation (MLE) using hands-on example. Know the importance of log likelihood function and its use in estimation problems. Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a statistical model. The core idea behind MLE is to find the parameter values that maximize the likelihood … Read more
Keywords: maximum likelihood estimation, statistical method, probability distribution, MLE, models, practical applications, finance, economics, natural sciences. Introduction Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a probability distribution by finding the set of values that maximize the likelihood function of the observed data. In other words, MLE is … Read more