Abstract
In this paper we address two frequency estimation algorithms, Moment Method and Markov Chain Monte Carlo method (MCMC). The advantage of Moment Method estimator is easy to determine and simple to implement. It don’t need the distribution of parameters. The disadvantage is that its solution is not only. Hence, the solution is not surely with good property. In this paper, we produce the Moment Method estimator with three cases, known signal amplitude, unknown signal amplitude and high SNR, unknown signal amplitude and lower SNR. Markov Chain Monte Carlo method builds a series of Markov chain with repeating sampling to get an approximate distribution. Markov Chain Monte Carlo method uses two algorithms, Metropolis-Hastings algorithm and Gibbs sampler.The advantage of MCMC is that it is a demonstrated way to deal with more parameter numbers and the result has surely good property. The disadvantage is that MCMC must generate a probability distribution function for parameters. Hence, we will get the bad result with wrong assumption of probability distribution function.