Questions tagged [maximum-a-posteriori-estimation]

Use this tag for any questions regarding or utilizing the Maximum a Posteriori Estimation (MAP) method.

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Understanding the Difference Between MAP Estimation and ML Estimation

There are a number of possible criteria to use in making decisions. Can someone elaborate on the difference between ML and MAP for a sequence of BPSK symbols impaired by Gaussian noise ?
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1answer
514 views

Intuitive Meaning of Regularization in Imaging Inverse Problems

Hello Every one I have been trying to understand the intuitive meaning of using a regularizer in images. Specifically what does the Total Variation regularizer do in images and how is it able to ...
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1answer
115 views

Notations to Use in Formulating of Maximum Likelihood Estimation

The received noisy signal $y_n \in \mathbb{R}$ is expressed as: \begin{align} y_n = \mathbf{h}^\mathsf{T}\mathbf{u}_n + w_n. \tag{1} \end{align} $\mathbf{h} = [h_0,h_1,\ldots,h_{p-1}]^\mathsf{T} \in \...
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1answer
382 views

General questions on Kalman filter and difference

In the wikipedia Kalman filter link, the state variable $x_k$ takes a continuous value say a floating point number, but what if the values are integer say symbols from an alphabet set, then how does ...
3
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1answer
592 views

How to Use Maximum a Posteriori Probability (MAP) in Classification Task

I have a 2D image defined on a region $\Omega$. Let $I: \Omega \to R$ be a gray image. Assume that the region can be separated into $N$ sub-regions $\Omega_i$ such that $$\forall i,j=1,\ldots ,N:\...
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1answer
107 views

Performance of Viterbi detector over non-minimum phase channels

For sequences that are transmitted over channels with memory $\mu=n$ and response H=$[h_0 h_1 \ldots h_n]$,Viterbi algorithm implements Maximum Likelihood (ML) detection and BCJR implements Maximum A-...
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1answer
174 views

Maximum Likelihood Derivation

as you can see on page 314 the derivation of the ln[f etc.] equation is done via the matched filter that i have also provided below. i would like to know how this equation is used to derive the ln[f ...
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1answer
86 views

Analogous filter to Kalman that maximizes mode (as opposed to minimizing variance)

I may have a potential application where maximizing the mode (as opposed to typically minimizing the variance) would be useful for state estimates. The situation may arise from skewed distributions ...
3
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2answers
3k views

Differences Using Maximum Likelihood or Maximum a Posteriori for Deconvolution / Deblur

Are there any differences if you use Maximum Likelihood or Maximum a Posteriori to estimate the Point Spread Function for image deconvolution?
11
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1answer
343 views

Recover curves from noisy collection of points

Background: I'm trying to make a system that tracks a number of bubbles in a video I'm implementing the bubble detection in the single image case using the Circular Hough Transform. Due to occlusion, ...
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3answers
1k views

Determining optimal binary decision rule threshold from observations with unknown priors?

Given only observations of a binary signal perturbed by Gaussian noise with unknown prior information, how can I estimate the optimal decision threshold? ( No, this is not a homework question) ...