Qing Zhao
@QingZhao20
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I use R.
Fort Collins, CO
Joined April 2019
Received a hard copy of my book. Still can’t believe I actually did it! 💪 All code is available on https://t.co/0FJsB5oziS
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Successfully used NUTS (the Stan stuff) in N-mixture models, thanks to the flexibility of @R_nimble. Basically I just used NUTS on continuous parameters and retained the slice sampler for N. NUTS is actually slower (72 sec) than MH (31 sec), but results in better mixed chains.
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I knew it!
Scientists have captured Earth’s climate over the last 485 million years. Here’s the surprising place we stand now.
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This is useful stuff
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The art technique of Grandma Mei Ling, age 82 Wait for it
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Bird populations have been declining for decades, with three billion birds lost over the last 50 years. Understanding the drivers of these loses is a monumental task. Read on at the link below about how we are integrating data to understand these drivers https://t.co/eyZabswO8L
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📖Published📖 Zhao et al. developed an integrated dynamic N-mixture model that jointly analyses rigorous survey and participatory science data to inform population growth at broad spatiotemporal extents 👇 https://t.co/V7cVo7yEYg
besjournals.onlinelibrary.wiley.com
Knowledge of variation in population processes (e.g. population growth) across broad spatiotemporal scales is fundamental to population ecology and critical for conservation decision-making. Count...
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While the approach can be used in other abundance-based models, we illustrate it with Hostetler & Chandler’s dynamic N-mixture model which allows in-depth understanding of the spatiotemporal variation of population processes.
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The cool thing about this approach is that it can tease out the biases and errors in cs data so that we can use them to expand the spatial/temporal extent of the inference without lowering its quality.
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New paper: an approach for combining distance sampling and citizen science data developed by myself, @latimerchris1 and other folks at @BirdConservancy.
besjournals.onlinelibrary.wiley.com
Knowledge of variation in population processes (e.g. population growth) across broad spatiotemporal scales is fundamental to population ecology and critical for conservation decision-making. Count...
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Ok I thought I figured out how to do it in parallel; the code did run, but it took about the same time as running three chains sequentially, so what’s really going on here?
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A dynamic occupancy model, Rcpp reduces the computing time from 75 minutes in R to <11 minutes 🥳
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Our little raccoon is on the cover now! Check out the story about potential human-wildlife coexistence through education ➡️
besjournals.onlinelibrary.wiley.com
Cover image: A raccoon dog roaming in Shanghai urban environment. People's familiarity with raccoon dogs positively influenced their tolerance. Education programs that familiarize the public with...
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A static occupancy model in Rcpp, reducing the running time (under a certain sample size and number of iterations) from 3.38 minutes in R to 28 seconds. Now we are talking.
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Successfully coded up a logistic regression in Rcpp💪which is faster than R👏finally!
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