# Tagged Questions

Estimation theory is a branch of statistics and signal processing that deals with estimating the values of parameters based on measured/empirical data that has a random component.

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### Literature study for Optimal Estimation Theory

It seems Optimal Estimation/Control Theory requires a lot more than undergraduate maths. Any good book that would help me get started? I have so far referred the following books but found them quite ...
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### Bayesian Estimation: calculating an integral

I am reading a book on Bayesian filtering and I have a question regarding calculating transition density $p(X_t|X_{t-1})$. My question is how the term $p(X_t|X_{t-1}, V_{t}=v)$ is converted to the ...
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### 'Bounds' on the Covariance Matrix

We define covariance of random vector ${\bf X}$ as \begin{align} Cov({\bf X})=E \left[ \left( {\bf X}-E[{\bf X}] \right) \left( {\bf X}-E[{\bf X}] \right)^T \right]. \end{align} In the scalar case ...
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### A question about Estimation problem in digital communication setup.

I originally asked this problem here http://dsp.stackexchange.com/questions/31503/estimation-problem-for-m-ary-pam-transmission-over-awgn-channel-problem I would appreciate if someone can take a ...
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### Asymptotically unbiased estimator for 1/p in Bernouilli distribution?

Suppose I have a sample of $n$ independent stochastic variables, each Bernouilli distributed with parameter $p$ (you may assume $0 < p <1$). I was wondering if there exist (asymptotically) ...
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### Variance of sample mean (problems with proof)

Assuming that I have $\{x_1,\ldots, x_N\}$ - an iid (independent identically distributed) sample size $N$ of observations of random variable $\xi$ with unknown mean $m_1$, variance (second central ...
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### How to use MLE method for non-distribution function?

I understand that maximum likelihood estimation (MLE) method is normally used with distribution function. However is there anyway around I can do to use MLE for a function which is not distribution ...
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### Fisher's information better than covariance matrix in estimators

I know Fishers information matrix is the inverse of covariance matrix. But why is it better to use fishers information matrix instead of covariance matrix in the case of distributed sensor networks?