Yun-Xiao Li / 李云箫
@yunxiao-li.bsky.social
Name pronunced: yewn-shyao lee | PhD student in Psychology 👨🏻🎓| Drummer 🥁 | Golden Age Mystery lover 🕵🏻♂️
Reposted by Yun-Xiao Li / 李云箫
Welcome to Dr Yun-Xiao Li, who will be investigating how we make choices about risk and the future in his ESRC-funded postdoc with Dr. Manos Konstantinidis. Yun-Xiao has not travelled far: he did his PhD in our department on discreteness in mental sampling and decision making.
December 9, 2025 at 1:31 PM
Welcome to Dr Yun-Xiao Li, who will be investigating how we make choices about risk and the future in his ESRC-funded postdoc with Dr. Manos Konstantinidis. Yun-Xiao has not travelled far: he did his PhD in our department on discreteness in mental sampling and decision making.
Reposted by Yun-Xiao Li / 李云箫
Exciting #rstats news for Bayesian model comparison: bridgesampling is finally ready to support cmdstanr, see screenshot. Help us by installing the development version of bridgesampling and letting us know if it works for your model(s): pak::pkg_install("quentingronau/bridgesampling#44")
![R code and output showing the new functionality:
``` r
## pak::pkg_install("quentingronau/bridgesampling#44")
## see: https://cran.r-project.org/web/packages/bridgesampling/vignettes/bridgesampling_example_stan.html
library(bridgesampling)
### generate data ###
set.seed(12345)
mu <- 0
tau2 <- 0.5
sigma2 <- 1
n <- 20
theta <- rnorm(n, mu, sqrt(tau2))
y <- rnorm(n, theta, sqrt(sigma2))
### set prior parameters ###
mu0 <- 0
tau20 <- 1
alpha <- 1
beta <- 1
stancodeH0 <- 'data {
int<lower=1> n; // number of observations
vector[n] y; // observations
real<lower=0> alpha;
real<lower=0> beta;
real<lower=0> sigma2;
}
parameters {
real<lower=0> tau2; // group-level variance
vector[n] theta; // participant effects
}
model {
target += inv_gamma_lpdf(tau2 | alpha, beta);
target += normal_lpdf(theta | 0, sqrt(tau2));
target += normal_lpdf(y | theta, sqrt(sigma2));
}
'
tf <- withr::local_tempfile(fileext = ".stan")
writeLines(stancodeH0, tf)
mod <- cmdstanr::cmdstan_model(tf, quiet = TRUE, force_recompile = TRUE)
fitH0 <- mod$sample(
data = list(y = y, n = n,
alpha = alpha,
beta = beta,
sigma2 = sigma2),
seed = 202,
chains = 4,
parallel_chains = 4,
iter_warmup = 1000,
iter_sampling = 50000,
refresh = 0
)
#> Running MCMC with 4 parallel chains...
#>
#> Chain 3 finished in 0.8 seconds.
#> Chain 2 finished in 0.8 seconds.
#> Chain 4 finished in 0.8 seconds.
#> Chain 1 finished in 1.1 seconds.
#>
#> All 4 chains finished successfully.
#> Mean chain execution time: 0.9 seconds.
#> Total execution time: 1.2 seconds.
H0.bridge <- bridge_sampler(fitH0, silent = TRUE)
print(H0.bridge)
#> Bridge sampling estimate of the log marginal likelihood: -37.73301
#> Estimate obtained in 8 iteration(s) via method "normal".
#### Expected output:
## Bridge sampling estimate of the log marginal likelihood: -37.53183
## Estimate obtained in 5 iteration(s) via method "normal".
```](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:lgxrkw556dtxkplwrj5rzlli/bafkreicf6zdyyjmaxrdfyq2sekokprym3c2o5atca6i2cczcx5zec4q4hm@jpeg)
September 2, 2025 at 9:16 AM
Exciting #rstats news for Bayesian model comparison: bridgesampling is finally ready to support cmdstanr, see screenshot. Help us by installing the development version of bridgesampling and letting us know if it works for your model(s): pak::pkg_install("quentingronau/bridgesampling#44")
Reposted by Yun-Xiao Li / 李云箫
#rstats #statistics
I've released my new open source book, "Powered by Linear Algebra: the role of matrices and vector space in data science," at matloff.github.io/WackyLinearA....
Turns the classic LA course on its head! Still proves the theorems, but with a deep emphasis on applications.
I've released my new open source book, "Powered by Linear Algebra: the role of matrices and vector space in data science," at matloff.github.io/WackyLinearA....
Turns the classic LA course on its head! Still proves the theorems, but with a deep emphasis on applications.
matloff.github.io
August 15, 2025 at 4:53 PM
#rstats #statistics
I've released my new open source book, "Powered by Linear Algebra: the role of matrices and vector space in data science," at matloff.github.io/WackyLinearA....
Turns the classic LA course on its head! Still proves the theorems, but with a deep emphasis on applications.
I've released my new open source book, "Powered by Linear Algebra: the role of matrices and vector space in data science," at matloff.github.io/WackyLinearA....
Turns the classic LA course on its head! Still proves the theorems, but with a deep emphasis on applications.
Reposted by Yun-Xiao Li / 李云箫
Our lab has a list of papers that use statistical sampling algorithms like MCMC to explain human behaviour. Thanks to @lcastillo.bsky.social, you can select by behaviour or algorithm.
If we've missed any, please let us know!
sampling.warwick.ac....
If we've missed any, please let us know!
sampling.warwick.ac....
May 16, 2025 at 11:19 AM
Our lab has a list of papers that use statistical sampling algorithms like MCMC to explain human behaviour. Thanks to @lcastillo.bsky.social, you can select by behaviour or algorithm.
If we've missed any, please let us know!
sampling.warwick.ac....
If we've missed any, please let us know!
sampling.warwick.ac....
Had a great time working with Lucas and Adam on this preprint and the package! Let us know what you think!
🚀🚀 Very excited about this new preprint with @yunxiao-li.bsky.social and @asanborn.bsky.social!
Months ago we released the samplr package on CRAN (helps you use sampling algorithms + cogn. models for human data). Here we explain the theoretical background and show how to use the pkg
osf.io/ax8hm
Months ago we released the samplr package on CRAN (helps you use sampling algorithms + cogn. models for human data). Here we explain the theoretical background and show how to use the pkg
osf.io/ax8hm
OSF
osf.io
May 21, 2025 at 10:33 AM
Had a great time working with Lucas and Adam on this preprint and the package! Let us know what you think!
Reposted by Yun-Xiao Li / 李云箫
Thrilled to see my first publication out in PB&R with @sotarokita.bsky.social and @suzanneaussems.bsky.social! Adults interpret novel verbs using iconic speed cues in speech prosody and hand gesture, with a small but reliable link across modalities.
Open access: link.springer.com/10.3758/s134...
Open access: link.springer.com/10.3758/s134...
Adults interpret iconicity in speech and gesture via the same modality-independent process - Psychonomic Bulletin & Review
Iconicity is the resemblance or similarity between the form of a signal and its meaning. In two studies, we investigated whether adults interpret iconicity in speech and gesture via a modality-indepen...
link.springer.com
May 13, 2025 at 11:16 AM
Thrilled to see my first publication out in PB&R with @sotarokita.bsky.social and @suzanneaussems.bsky.social! Adults interpret novel verbs using iconic speed cues in speech prosody and hand gesture, with a small but reliable link across modalities.
Open access: link.springer.com/10.3758/s134...
Open access: link.springer.com/10.3758/s134...
Reposted by Yun-Xiao Li / 李云箫
5 stars is better than 4 stars, but can we even define how much better it might be? Modeling ordinal outcomes like ratings is a subtle topic; fortunately I have a new chapter that dives directly into that nuance.
HTML: betanalpha.github.io/assets/chapt...
PDF: betanalpha.github.io/assets/chapt...
HTML: betanalpha.github.io/assets/chapt...
PDF: betanalpha.github.io/assets/chapt...
Ordinal Modeling
betanalpha.github.io
March 17, 2025 at 1:58 PM
5 stars is better than 4 stars, but can we even define how much better it might be? Modeling ordinal outcomes like ratings is a subtle topic; fortunately I have a new chapter that dives directly into that nuance.
HTML: betanalpha.github.io/assets/chapt...
PDF: betanalpha.github.io/assets/chapt...
HTML: betanalpha.github.io/assets/chapt...
PDF: betanalpha.github.io/assets/chapt...
Reposted by Yun-Xiao Li / 李云箫
New paper with Tong Liu and Arndt Broeder, just accepted in Cognition. We test novel qualitative predictions from sampling-based models of probability estimation in an event ranking task. Results provide evidence for the idea that mental sampling underlies probability judgements.
Evaluating the Role of Mental Sampling in Probability Judgments: Illogical Rankings Occur in a Predictable Manner: https://osf.io/bfwpe
March 19, 2025 at 9:38 PM
New paper with Tong Liu and Arndt Broeder, just accepted in Cognition. We test novel qualitative predictions from sampling-based models of probability estimation in an event ranking task. Results provide evidence for the idea that mental sampling underlies probability judgements.
Reposted by Yun-Xiao Li / 李云箫
"Noise in Cognition: Bug or Feature?" is now available in Perspectives on Psychological Science
doi.org/10.1177/1745... (1/4)
doi.org/10.1177/1745... (1/4)
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doi.org
March 4, 2025 at 10:33 PM
"Noise in Cognition: Bug or Feature?" is now available in Perspectives on Psychological Science
doi.org/10.1177/1745... (1/4)
doi.org/10.1177/1745... (1/4)
🚨 A new preprint is out!
How does utility influence mental simulations of risky events? 🤔🎲
We tested this across 4 experiments & found that most people simulate probabilities accurately, but biases emerge in key conditions!
If you want to learn more, keep reading!
doi.org/10.31234/osf...
How does utility influence mental simulations of risky events? 🤔🎲
We tested this across 4 experiments & found that most people simulate probabilities accurately, but biases emerge in key conditions!
If you want to learn more, keep reading!
doi.org/10.31234/osf...
OSF
doi.org
February 20, 2025 at 6:42 PM
🚨 A new preprint is out!
How does utility influence mental simulations of risky events? 🤔🎲
We tested this across 4 experiments & found that most people simulate probabilities accurately, but biases emerge in key conditions!
If you want to learn more, keep reading!
doi.org/10.31234/osf...
How does utility influence mental simulations of risky events? 🤔🎲
We tested this across 4 experiments & found that most people simulate probabilities accurately, but biases emerge in key conditions!
If you want to learn more, keep reading!
doi.org/10.31234/osf...
Reposted by Yun-Xiao Li / 李云箫
New Preprint Out! 🚀🚀
Can people generate a random sequence if given enough time?
Keep reading if
- You make cognitive models with randomness in them
- You like to explore the world, be creative, choose well
- You want protection from clever agents exploiting patterns in your behavior.
osf.io/awg9j
Can people generate a random sequence if given enough time?
Keep reading if
- You make cognitive models with randomness in them
- You like to explore the world, be creative, choose well
- You want protection from clever agents exploiting patterns in your behavior.
osf.io/awg9j
OSF
osf.io
February 20, 2025 at 11:14 AM
New Preprint Out! 🚀🚀
Can people generate a random sequence if given enough time?
Keep reading if
- You make cognitive models with randomness in them
- You like to explore the world, be creative, choose well
- You want protection from clever agents exploiting patterns in your behavior.
osf.io/awg9j
Can people generate a random sequence if given enough time?
Keep reading if
- You make cognitive models with randomness in them
- You like to explore the world, be creative, choose well
- You want protection from clever agents exploiting patterns in your behavior.
osf.io/awg9j