Questions tagged [maximum-a-posteriori-estimation]
Use this tag for any questions regarding or utilizing the Maximum a Posteriori Estimation (MAP) method.
12
questions
0
votes
0
answers
46
views
Modelling problem
Considering a finite-length impulse response $h[n]$ of length $M $ (which amounts to considering $h[n] = 0,$ for $n \geq M$). The data model, with additive disturbance, is then written as:
$$y[n] = ...
- 1
5
votes
3
answers
6k
views
Understanding the Difference Between MAP Estimation and ML Estimation
There are a number of possible criteria to use in making decisions.
Can someone elaborate on the difference between ML and MAP for a sequence of BPSK symbols impaired by Gaussian noise ?
- 155
4
votes
1
answer
643
views
Intuitive Meaning of Regularization in Imaging Inverse Problems
Hello Every one I have been trying to understand the intuitive meaning of using a regularizer in images. Specifically what does the Total Variation regularizer do in images and how is it able to ...
- 43
1
vote
1
answer
129
views
Notations to Use in Formulating of Maximum Likelihood Estimation
The received
noisy signal $y_n \in \mathbb{R}$ is expressed as:
\begin{align}
y_n = \mathbf{h}^\mathsf{T}\mathbf{u}_n + w_n.
\tag{1}
\end{align}
$\mathbf{h} = [h_0,h_1,\ldots,h_{p-1}]^\mathsf{T} \in \...
- 616
-1
votes
1
answer
678
views
General questions on Kalman filter and difference
In the wikipedia Kalman filter link, the state variable $x_k$ takes a continuous value say a floating point number, but what if the values are integer say symbols from an alphabet set, then how does ...
- 849
4
votes
1
answer
823
views
How to Use Maximum a Posteriori Probability (MAP) in Classification Task
I have a 2D image defined on a region $\Omega$. Let $I: \Omega \to R$ be a gray image. Assume that the region can be separated into $N$ sub-regions $\Omega_i$ such that $$\forall i,j=1,\ldots ,N:\...
- 575
2
votes
1
answer
121
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-...
- 187
0
votes
1
answer
235
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 ...
- 11
1
vote
1
answer
95
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 ...
- 28
5
votes
2
answers
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?
- 91
11
votes
1
answer
350
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, ...
- 113
8
votes
3
answers
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)
...
- 2,950