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Bayequentist

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About Bayequentist

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  1. That sounds insane to me. How many people are there in your cohort that can pool together 2 million? Was there any funding or was it completely self-funded? Maybe a lot of people at Harvard are just independently rich? 😅
  2. I'd recommend one of the new ThinkPads with Ryzen 4000 CPUs that are coming out in the next 2 weeks. If you choose the L15 (starting at $649), you can stack up on RAM and SSD and the price would only be around $1k. The AMD's 7nm Ryzen processors, despite being cheaper, are thrashing Intel CPUs right now in terms of multi-core performance, which is very crucial if you do a lot of parallel computations. In terms of build quality and longevity, ThinkPads are the OG business laptops that will last you many years. GPUs are not really important, because even if you want to do deep learning, you'd want to use your university's computing cluster (my uni has a NVIDIA DGX-2 cluster available upon request) or use some cloud solution.
  3. On a side note, does anyone know when will US News refresh the rankings for stat/biostat?
  4. Agree that reaching out might help, but it won't help most of the time. From the pinned post by cyberwulf: Funding in most (but not all) U.S. stat/biostat programs is allocated at the department level to the strongest incoming students, so applicants aren't typically "matched" to potential advisors who agree to fund them*. Rather, the department projects the total number of positions available and then tries to recruit up to that number of students. Once the students are on campus, they are then either assigned to a position or (ideally) have some choices available to them. Of course OP should still try and reach out to faculty (but don't expect anything). Regarding GRE subject test, OP did not take Abstract Algebra, Real and Complex Analysis, so taking the test will most likely mean throwing money away. Still, if OP is independently wealthy and willing to give it a shot then by all means go ahead and take the test.
  5. Something to consider: given your strong background in math and stat (for a program like Oregon State), it should be no problem for you to get straight A’s in Casella & Berger.
  6. In recent years I've seen quite a few stats PhD programs popping up that don't have coursework requirements for advanced statistical theory (Lehman & Casella...) and measure-theoretic probability (Durrett...) - e.g. programs that focus on Bayesian/computational/high-dimensional statistics and statistical learning. What are thoughts on those programs? Pros/cons in terms of academia/industry?
  7. Admission committees in top schools receive a lot of >80% GRE scores from international students every year... so I'd say only submit it if they require it. 71% is pretty good for top 25~50 schools, but your profile is already competitive for those schools even without the score.
  8. Besides classes, GRE General/Subject, it'd be awesome if you can do a REU this coming summer. A good LoR from a research advisor will strengthen your profile a lot.
  9. If you can show admission committee that you know linear algebra, I think you'd have a good chance of getting into Oregon State's MS Statistics program, with funding. It'd be a good stepping stone to a better PhD program.
  10. I think DanielWarlock was a little confused. OP never stated that he wants a PhD in Statistics. Almost all of the materials taught in a typical MS Statistics program are not measure-theoretic (the probability course might touch upon a little bit of σ-algebra).
  11. IIRC Stat PhD Now Postdoc mentioned that he did his PhD at UF. You can search the database of gradcafe for GRE scores: https://www.thegradcafe.com/survey/index.php?q=uiuc+statistics and https://www.thegradcafe.com/survey/index.php?q=university+of+florida+statistics.
  12. In addition to numerical analysis/optimization, I think you should take a legit CS class, like Data Structures & Algorithms or Parallel Programming.
  13. Aside from Mathematical Statistics, a course on Stochastic Processes is also a common follow-up to Stat 110. Since galois seems to prefer watching lecture videos to reading books, fast.ai has a pretty interesting course on computational linear algebra.
  14. The authors did publish a more recent book: Computer Age Statistical Inference, which has a better balance between frequentist and Bayesian approaches. There are also roughly 20 pages on Neural Networks and Deep Learning.
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