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Prikvačeni tweet
- Highly significant effect - Fixed cost of implementing treatment - Diminishing returns on loss function to treatment - Evaluating your loss function at expected TE tells you the wrong thing to do. ABIYLFOYP Always Be Integrating Your Loss Function Over Your Posteriorpic.twitter.com/r1xTGkOmQB
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Imagine that we privatized non-primary streets. Local + service vehicles only, bollarded. Residents would love it, but it would make driving impossible. I wonder if there’s a lesson here about the role of private groups in the death of Facebook? Will DM groups kill Twitter?
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Great plots too. Are there any non-academic think-tanks anywhere that produce work like this? Demos or Brookings, maybe?https://twitter.com/chrisedmond/status/1224208068839264257 …
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Savage Jim proslijedio/la je Tweet
Super bowl sponsorship by Google - are people like “yeah, maybe I will google something”
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Saw Uncut Gems tonight. Fantastic film. Great story, but also technically brilliant. Casting, costumes, lighting, sound, music: all 5/5. Just delicious.
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Savage Jim proslijedio/la je Tweet
Car-free zones I'd like to see in NYC, LA, SF, and Boston. Could start with "only local residents and workers get a car permit to enter the area", and speed limit is very low, to begin with.pic.twitter.com/bxunRIYbuD
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In my world (expat Australians in tech/econ/policy),
@NGruen1 because of his willingness to have very long lunches with interesting strangers followed by dozens of follow-up emails sharing essays.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Does your community have any people that everyone seems to consider a mentor or coach? Who are they, and what makes their approach special?
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Useful
@michael_nielsen! 1- nice blog post. 2- sometimes you’re on a public WiFi and it won’t bring up the login window. In those cases, you can usually prompt the login window to pop up by navigating to a non-https website, but there aren’t many left. http://michaelnielsen.org https://twitter.com/yudapearl/status/1223592867588800512 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
On reporting rates--this is a real challenge! I'm sure there are clever people who do this for a living, but I'd use plug-in rates for sensitivity checking. It just doesn't seem like a easy thing to estimate. Have a great weekend!
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On dist choice: one nice fact is that a mixture of normal densities has a mixture of normal CDF. So you can use mixtures of normals for your f()s to capture most of the weirdness going on in unusual arrival time distributions, while staying analytically convenient.
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log_sum_exp(a, b) = log(exp(a) + exp(b)). So log(θ*(1 - F(d|t))+ (1 -θ)*(1-F(c|t))) = log_sum_exp(log(θ) + log(1 - F(d|t)), log(1-θ) + log(1 - F(c|t))). Beautiful! Two more challenges: what distributions for f(d|t) and f(c|t); and what about low reporting?
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If they're neither dead nor alive, a bit more tricky. Then it's the probability they're going to die but haven't yet, plus the probability they'll live but haven't been cleared yet. So θ*(1 - F(d|t))+ (1 -θ)*(1-F(c|t)). Taking logs of that is a bit nasty, so we use log_sum_exp
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So what's the likelihood of observing this data for a patient, p(s,t|θ)? If they're dead, it's θ*f(d|t), so log likelihood contribution is log(θ) + log(f(d|t)) If they're cleared, it's (1-θ)*f(c|t); ll contribution = log(1-θ) + log(f(c|t)).
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Let's call f(d|t) the density of death arrival times t. f(c|t) is the density of dates that you get cleared. F() indicates CDF. θ is the death rate to be estimated. The data we need is each patient's state s (dead, sick, cleared), and days since infection, t.
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You wouldn't say that it's 100/200= .5. There are all these sick people who haven't died or ben cleared yet. Their survival so far contains useful information for our estimate. So how do we use it?
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For the moment, let's set reporting rates to 1. Why is the arrival time of death vs recovery an issue? Imagine the death rate is 20%. Deaths take 1 week on average, but rec takes 3 weeks. You infect 1000 people and wait two weeks; 100 die, and 100 are cured. What's your estimate?
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By the time the illness has passed, we could estimate its death rate given infection as (reported deaths + unrep'd deaths)/(rep'd deaths + unrep'd deaths + rep'd recoveries + unrep'd recs). 2 problems for us: unreported X isn't known, and deaths might arrive sooner than recovery
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It's a Friday night, so a good time to derive the likelihood function of a simple model to estimate the death rate from an illness that kills quickly but takes a long time to cure. Buckle up!
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Am I reading this right? Are 6-8% of US deaths due to flu and pneumonia?https://www.cdc.gov/flu/weekly/index.htm …
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An Australian social institution I’d like to introduce: the 6:30/7am breakfast catch-up. You meet someone at a conference and they’re passing through town? Totally acceptable to propose a 7am breakfast at a nice cafe. No alcohol, great food, and a natural end in case it’s awk.
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