Bayesian
What is the difference between frequentist and bayesian inferences ?
https://stats.stackexchange.com/a/56
Here is how I would explain the basic difference to my grandma:
I have misplaced my phone somewhere in the home. I can use the phone locator on the base of the instrument to locate the phone and when I press the phone locator the phone starts beeping.
Problem: Which area of my home should I search?
Frequentist Reasoning
I can hear the phone beeping. I also have a mental model which helps me identify the area from which the sound is coming. Therefore, upon hearing the beep, I infer the area of my home I must search to locate the phone.
Bayesian Reasoning
I can hear the phone beeping. Now, apart from a mental model which helps me identify the area from which the sound is coming from, I also know the locations where I have misplaced the phone in the past. So, I combine my inferences using the beeps and my prior information about the locations I have misplaced the phone in the past to identify an area I must search to locate the phone.
Response two is of interest also ! https://stats.stackexchange.com/a/1602
A Bayesian is one who, vaguely expecting a horse, and catching a glimpse of a donkey, strongly believes he has seen a mule
Stephen Senn, Statistician & Bayesian Skeptic (mostly)
https://faculty.washington.edu/kenrice/BayesIntroClassEpi2018.pdf
Application to Mandelbrot project
Bayesian are especially powerful for functional optimization. In the frame of the mandelbrot project we could try to functionalize our drug discovery quest < this alone is a big part of the problem.
- an ensemble of molecules found to be present in various amount in plants displaying a high inhibitory activity and a low cytotoxicity.
- the more this ensemble of molecules is restricted and unique to the highly bioactive plants, the more they are the potential responsible of this bioactivity.
- some priors could be defined: we know that scaffold X and Y usually display these activities.
Resources
podcasts
https://www.learnbayesstats.com/episodes/4#showEpisodes
readlist
See paper notes on Hidden Markov Models PMA8_147