Thoughts about Scientific Machine Learning and AI

LogP, LogD, pKa and LogS: A Physicists guide to basic chemical properties

logp
logd
pKa
logs
chemistry
drug discovery
molecular properties
Ever since I got into AI Based Drug Discovery a lot of terminology has been thrown my way. The idea to write this came from (Bhal 2021) which outlines that it’s important to…
Jan 6, 2024
Michael Green

What does the prior actually do in a Bayesian analysis?

bayesian
prior
posterior
likelihood
I often feel when talking to coworkers, practitioners and people in general that the Bayesian philosophy has to prove it’s worth or justification as if it’s a contender to…
Dec 6, 2020
Michael Green

COVID-19 in Denmark - An epidemic in a small country

epidemiology
corona
virus
model
derivative
differentialequations
pandemic
The COVID-19 virus has struck the world with a rare force (Hui et al. 2020). In a matter of months it has affected 194 countries and territories so far. With respect to…
Mar 17, 2020
Michael Green

A quick introduction to derivatives for machine learning people

non-linear
modeling
regression
math
ml
If you’re like me you probably have used derivatives for a huge part of your life and learned a few rules on how they work and behave without actually understanding where it…
Feb 9, 2018
Michael Green

The importance of context

inference
bayesian
frequentist
regression
maximum likelihood
When we do modeling it’s of utmost importance that we pay attention to context. Without context there is little that can be inferred.
Feb 1, 2018
Dr. Michael Green

Deep Neural Networks in Julia - Love at first sight?

programming
julia
modeling
deep learning
I love new initiatives that tries to do something fresh and innovative. The relatively new language Julia is one of my favorite languages. It features a lot of good stuff in…
Jan 10, 2018
Michael Green

On the apparent success of the maximum likelihood principle

inference
bayesian
frequentist
regression
maximum likelihood
Today we will run through an important concept in statistical learning theory and modeling in general. It may come as no surprise that my point is as usual “age quod agis”.…
Jul 28, 2017
Dr. Michael Green

Building and testing a simple deep learning object detection application

object detection
deep learning
coco
Deep learning is hot currently. Really hot. The reason for this is that there’s more data available than ever in the space of perception. By perception I mean tasks such as…
Jul 15, 2017
Dr. Michael Green

About identifiability and granularity

prior
regression
marketing mix modeling
modeling
bayesian
variable selection
In time series modeling you typically run into issues concerning complexity versus utility. What I mean by that is that there may be questions you need the answer to but are…
May 6, 2017
Dr. Michael Green

A few thoughts on apparent bimodality for regression problems!

prior
regression
modeling
bayesian
bimodality
Did you ever run into a scenario when your data is showing two distinctive relationships but you’re trying to solve for it with one regression line? This happens to me a…
Apr 9, 2017
Dr. Michael Green

 

On the equivalence of Bayesian priors and Ridge regression

prior
regression
modeling
bayesian
Today I’m going to take you through the comparison of a Bayesian formalism for regression and compare it to Ridge regression which is a penalized version of OLS. The…
Jan 18, 2017
Dr. Michael Green

About choosing your optimization algorithm carefully

optimization
simulation
non-linear
evolutionary
constrained optimization
Why is simulation important anyway? Well, first off we need it since many phenomemna (I would even say all interesting phenomemna) cannot be encapsulated by a closed form…
Oct 17, 2016
Dr. Michael Green

The truth about priors and overfitting

prior
regression
modeling
bayesian
hypothesis testing
Have you ever thought about how strong a prior is compared to observed data? It’s not an entirely easy thing to conceptualize. In order to alleviate this trouble I will take…
Aug 31, 2016
Dr. Michael Green

The insignificance of significance

significance
statistics
p-value
bayesian
hypothesis testing
Statistical significance has always held a slightly magical status in the research community as well as in every other community. This position is unwarrented and the trust…
Jun 15, 2016
Dr. Michael Green

Doing price optimization in R

optimization
simulation
non-linear
elasticity
price
econometrics
As many of us already know R is an extremely useful and powerful language for designing, building and evaluating statistical models. In this example I’m going to use R for…
May 6, 2016
Dr. Michael Green

About confidence intervals

confidence intervals
bayesian
credible interval
regression
Confidence intervals are as beautiful as they are deceiving. They’re part of an elegant theory of mathematical statistics which has been abused since the dawn of time. Why…
May 5, 2016
Dr. Michael Green
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