Readit NewsBut this is my 5th job out of college and the longest I've been in a single job. When I hit my 4year mark, I started to think that the grass is greener on the other side and looked for opportunities here and there.
A few years in and I didn't find the right place that would make me jump ship. I have high standards and I can spot redflags based on past experiences in jobs in multiple countries.
I also found out that after I got older and accrue more responsibilities outside work, my job became a much smaller focal point of my life.
I'd rather be employed in an okay place, being paid a competitive enough salary (75% in the curve), and have opportunity to learn new things in the job and out, even if I don't love the field. As opposed to try a new job and risk being put in a toxic work environment and lose the balance I have now.
If you are not growing as an engineer, going to a new job might not help you and could be detrimental. It's much better to learn new things while you have the time to do so. Find an area you want to learn more, try new projects or courses, and have fun at your own pace.
Regarding your question, first, I'd like to understand what problem you want to solve, and whether this approach will be useful for other users of tea-tasting.
At my company we have very time sensitive AB tests that we have to run with very few data points (at most 20 conversions per week, after 1000 or so failures).
We found out that using Bayesian A/B testing was excellent for our needs as it could be run with fewer data points than regular AB for the sort of conversion changes we aim for. It gives a probability of group B converting better than A, and we can run checks to see if we should stop the test.
Regular ABs would take too long and the significance of the test wouldnt make much sense because after a few weeks we would be comparing apples to oranges.
I'm wondering if you'd like to accept a contribution for Bayesian AB Testing, based on this whitepaper[0] and developed in Numpy.
If so, we can chat at my email gbenatt92 at zohomail dot com, or I can open a draft PR to discuss the code and paper.
[0]https://vwo.com/downloads/VWO_SmartStats_technical_whitepape...
[1] https://www.microsoft.com/en-us/research/publication/pattern...
Giving a cursory look into Bishop's book I see that I am wrong, as there's deep root in Bayesian Inference as well.
On another note, I find it very interesting that there's not a bigger emphasis on using the correct distributions in ML models, as the methods are much more concerned in optimizing objective functions.
Edit: corrected my sentence, but see 0xdde reply for better info.
The article gave me the same vibe, nice, short set of labels for me to apply as a heuristic.
I never really understood this particular war, I'm a simpleton, A in Stats 101, that's it. I guess I need to bone up on Wikipedia to understand what's going on here more.
I’m still convinced that Americans tend to dislike the frequentist view because it requires a stronger background in mathematics.
To understand both camps I summarize like this.
Frequentist statistics has very sound theory but is misapplied by using many heuristics, rule of thumbs and prepared tables. It's very easy to use any method and hack the p-value away to get statistically significant results.
Bayesian statistics has an interesting premise and inference methods, but until recently with the advancements of computing power, it was near impossible to do simulations to validate the complex distributions used, the goodness of fit and so on. And even in the current year, some bayesian statisticians don't question the priors and iterate on their research.
I recommend using methods both whenever it's convenient and fits the problem at hand.
The article is very well succinct and even explains why even my Bayesian professors had different approaches to research and analysis. I never knew about the third camp, Pragmatic Bayes, but definitely is in line with a professor's research that was very through on probability fit and the many iteration to get the prior and joint PDF just right.
Andrew Gelman has a very cool talk "Andrew Gelman - Bayes, statistics, and reproducibility (Rutgers, Foundations of Probability)", which I highly recommend for many Data Scientists
What are my limitations in this case? Can I keep the contract I'm currently in while the visa process chugs along? What about proof of income?