Maarten Marsman

@maartenmarsman.bsky.social

Assistant Professor at the University of Amsterdam

DigiD blijft Nederlands en dus veilig. Dat verzekerde de staatssecretaris van Binnenlandse Zaken na het nieuws over de mogelijke overname van leverancier Solvinity. Die zou geen toegang hebben tot de dienst, maar daar is niets van waar. En straks kan Trump dus mogelijk ook meekijken.

Amerikanen hebben straks wél toegang tot DigiD – ondanks belofte van staats­secretaris

Het IT-bedrijf achter DigiD komt mogelijk in Amerikaanse handen. Volgens staatssecretaris Eddie van Marum blijft DigiD desondanks ‘gewoon’ Nederlands en krijgen de Amerikanen geen toegang tot de perso...

ftm.nl

🚀 Our paper on Bayes factor tests for the differences in networks (graphical models) in two independent groups is now online at Psychometrika: doi.org/10.1017/psy.... Of course, these methods are implemented in the bgms R package, which now also allows testing more than two independent groups.

Bayes Factor Tests for Group Differences in Ordinal and Binary Graphical Models | Psychometrika | Cambridge Core

Bayes Factor Tests for Group Differences in Ordinal and Binary Graphical Models

doi.org

Bamojax (Bayesian modelling with JAX) v0.3.10 is out! Next to increased support for distributions and bijectors, several marginal likelihood estimators are now available, such as bridge sampling and truncated harmonic mean estimation (THAMES). Check it out on github.com/UncertaintyI...!

GitHub - UncertaintyInComplexSystems/bamojax: Bayesian Modelling using Jax

Bayesian Modelling using Jax. Contribute to UncertaintyInComplexSystems/bamojax development by creating an account on GitHub.

github.com

🚀 bgms 0.1.6.0 is now on CRAN! New in this release: • NUTS & HMC sampling for bgm() + bgmCompare() • Parallel chains + reproducible runs via seed • Markov chain diagnostics (ESS, R-hat) • New summary(), print(), and coef() methods 🔗https://cran.r-project.org/web/packages/bgms/index.html

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".
```

New paper with @richarddmorey.bsky.social now out in JASA, where we critically examine p-curve. Below is Richard’s excellent summary of the many poor statistical properties of p-curve (with link to paper). I wanted to add some conceptual issues that we also tackle in the paper.

Richard D. Morey@richarddmorey.bsky.social · 12mo ago

Paper drop, for anyone interested in #metascience, #statistics, or #metaanalysis! @clintin.bsky.social and I show in a new paper in JASA that the P-curve, a popular forensic meta-analysis method, has deeply undesirable statistical properties. www.tandfonline.com/doi/full/10.... 1/?

Cover page for the manuscript: Morey, R. D., & Davis-Stober, C. P. (2025). On the poor statistical properties of the P-curve meta-analytic procedure. Journal of the American Statistical Association, 1–19. https://doi.org/10.1080/01621459.2025.2544397

I am looking forward to expanding the scope of my professorship by combining cognitive and statistical modeling with LLMs😊 There will be two job openings for postdoc positions soon - one starting in September 2025 and another one a year later.

Universität Marburg@unimarburg.bsky.social · last yr.

🚀 930.000 Euro für #Psychologie -Forschung: Prof. Daniel Heck nutzt #KI, um psychologische Theorien präziser zu machen. Mithilfe von #LLMs sollen unscharfe Begriffe in messbare Modelle übersetzt werden – ein Schritt zu besseren Experimenten & klareren Erkenntnissen #Forschung uni-marburg.de/gDNQIi

Portrait Prof. Dr. Daniel Heck. Foto: Martin Schäfer

Are psychometric networks sufficiently supported by data such that one can be confident when interpreting its results? We analysed 294 psychometric networks from 126 papers with the Bayesian approach to address this question @jmbh.bsky.social Sara Ruth van Holst @maartenmarsman.bsky.social 🧵

PsyArXivBot@psyarxivbot.bsky.social · 2y ago

Statistical Evidence in Psychological Networks: A Bayesian Analysis of 294 Networks from 126 Studies: http://osf.io/62ydg/