In particular I’m pretty weak with Lie theory, so I’d love a book that introduces Lie theory in a way that is motivated by gauge theory. I also don’t know a lot of the required physics (Goldstein and Sakurai are about all I have).
You can assume grad-school level for me, but including gentle, conversational books would be very nice. For example, reading Baez is what got me interested in the subject.
I’m sure many of us are aware of the game F***, Marry, and Kill. I was thinking it could be fun to do the same except well, with cars. We can just change it to Hoon, Daily, and Destroy. Hoon lets you have 24 hours of any vehicle of your choosing. Daily is pretty self explanatory, choose any vehicle as your forever daily driver. Destroy lets you choose the vehicle you most despise and destroy it in any way you see fit.
My Picks would be:
Hoon – McLaren F1 – amazing car but would be too scared to daily this.
Daily – Audi Rs7 – perfomance, luxury, and beautiful styling tick all the boxes.
Destroy – my old Pontiac Grand AM Se – that car brought me nothing but headaches in the few short months I drove it. Method of execution would be to launch it right in the Atlantic Ocean of the steel pier in Atlantic City.
What would be your choices?
Hello can I ask you the difference between these two? I have a couple of questions:
- Can gradient discend be used to ”smooth” a non-stationary time serie? When I say ”smoothing” I mean the best fit that can describe the data, beside the regression method
2) I would like to know when it’ s better to use one instead of oneother… I know that gradient discend used in supervised learning, but can it be used also to predict a dependent variable of time serie?
What is the difference between the two?
Sorry I am at the beginning
Below is a practice problem i’m working on. I feel like I am really overthinking it and that is is actually very simple but i’m hitting a mental block. I’m also not confident in the answers I do think i’ve worked out?
b) [(1-3.25)^2 *.65] + [(1-3.25)^2 *.65] +[(1-3.25)^2 *.65] + [(1-3.25)^2 *.65] + [(1-3.25)^2 *.65]
d) P X > 3 (into binomial formula)
e) P X (greater than or equal to 3) into binomial formula
f) P X (greater than or equal to 1) into binomial formula
1. Suppose a certain treatment has a known response rate of 65% amongst adults. Further, suppose we randomly select 5 new adult patients to receive treatment. You may assume that these patients are independent.
a) How many patients would you expect to be responders?
b) What is the variance on the number of responders from the sample?
Calculate the following probabilities (c-f):
c) The first three patients are responders and the last two patients are not responders. (Note: Exactly this sequence)
d) Any three patients are responders and two are not responders
e) Three or more patients are responders.
f) At least one patient responds to the treatment.
g) Give two examples of how the assumptions of the binomial distribution could be violated if this were a real life situation.
Working with the package brms in for multilevel modeling, I’m trying to model a longitudinal observational study of disease progression in individuals, with a small subset of individuals opting for a particular treatment.
What I would like to test is if the timing of the initiation of that treatment relative to the initial diagnosis of the disease is significant in the disease progression. I would like to incorporate those that have the disease without surgery into the model in order to reap the benefits of mixed effect modeling.
What is the best approach here? I was thinking of doing some sort of nonlinear modeling or piecewise modeling but not sure how to approach either method.