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# System prepended metadata

title: '**Summary:**'

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# **Summary:**
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3314964/

**Theory:** 

The current iterative method of reconstructing 3d fourier spaces
from 2d images (single particle reconstruction), is prone to 
overfitting due to the high signal-to-noise ratio present in 
Cryo-EM images.  Current methods to counter this,  such as applying 
smoothness filtering, are largely based on heuristics and thus 
involve arbitrary decisions. 

To avoid this, one can use a statistical framework. Maximum 
Likelihood (ML) equations have shown to be effective, however 
they rely on large  datasets being present, and do not take
into account prior knowledge. As opposed to this,  Bayesian 
Maximum A Posteriori (MAP) estimation  utilizes previous data 
of smoothness, and does not rely on a large existing dataset. 

This paper utilises this MAP estimation technique with a Gaussian 
prior of smoothness in order to correctly estimate the level of 
smoothness at every resolution. A Gaussian distribution of noise
is a valid assumption, as conventional techniques make the same 
assumption.

**Summary of Results:**

MAP was tested in against the existing conventional
classification library, XMIPP, on 3 different datasets. MAP 
resulted in lower overfitting and greater information accuracy
across all resoutions, especially when the existing dataset was
insufficient. Some heuristics were employed when determining the 
characteristsics of the Gaussian smoothness prior. Although heuristics 
were employed, it was to a much lesser degree than the conventional
method.

In the future, the Bayesian framework can use data 
other than smoothness for the prior information input. Overall, 
the amount of arbitrariness was reduced and objectivity was increased.


# **Questions:**

Is using a Bayesian framework more/less computationally expensive?

Could a Bayesian approach potentially result in oversmoothening
on a very comprehensive dataset?













