- Jun 2023
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www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
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Children develop a succession of different, increasingly accurate, conceptions of the world and it at least appears that they do this as a result of their experience. But how can the concrete particulars of experience become the abstract structures of knowledge?
I was unfamiliar with Bayesian learning/Bayesian interference before reading this article. I looked it up and found a helpful tool here: https://seeing-theory.brown.edu/bayesian-inference/index.html. Much of the information I read to familiarize myself with the topic referred to it in the context of machine learning. I can see how the idea of "how one should update one’s beliefs upon observing data" can apply to student learning, especially for young kids. Kunin, D., Guo, J., Dae Devlin, T., & Ziang, D. (n.d.). Bayesian inference. Seeing Theory. https://seeing-theory.brown.edu/bayesian-inference/index.html
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- Jan 2023
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www.complexityexplorer.org www.complexityexplorer.org
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Hermeneutic circle In traditional humanities scholarship, the hermeneutic circle refers to the way in which we understand some part of a text in terms of our ideas about its overall structure and meaning -- but that we also, in a cyclic fashion, update our beliefs about the overall structure and meaning of a text in response to particular moments.
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- Aug 2022
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www.nature.com www.nature.com
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McDiarmid, A. D., Tullett, A. M., Whitt, C. M., Vazire, S., Smaldino, P. E., & Stephens, J. E. (2021). Psychologists update their beliefs about effect sizes after replication studies. Nature Human Behaviour, 5(12), 1663–1673. https://doi.org/10.1038/s41562-021-01220-7
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www.sciencedirect.com www.sciencedirect.com
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Corner, A., Hahn, U., & Oaksford, M. (2011). The psychological mechanism of the slippery slope argument. Journal of Memory and Language, 64(2), 133–152. https://doi.org/10.1016/j.jml.2010.10.002
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- Apr 2022
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twitter.com twitter.com
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ReconfigBehSci on Twitter: ‘RT @JASPStats: How to obtain introductory texts using the Learn Bayes Module in JASP. #stats #openSource https://t.co/dn7jyFr59i’ / Twitter. (n.d.). Retrieved 6 March 2021, from https://twitter.com/SciBeh/status/1326512599903694848
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- Aug 2021
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Pilditch, T. (2021). Why scientific evidence is no longer enough in public debate [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/98v2n
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- Jul 2021
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Romero, P., Mikiya, Y., Nakatsuma, T., Fitz, S., & Koch, T. (2021). Modelling Personality Change During Extreme Exogenous Conditions. PsyArXiv. https://doi.org/10.31234/osf.io/rtmjw
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- May 2021
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www.learnbayesstats.com www.learnbayesstats.com
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The ‘Learning Bayesian Statistics’ podcast. (n.d.). Retrieved 13 May 2021, from https://www.learnbayesstats.com
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- Apr 2021
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psyarxiv.com psyarxiv.com
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Elsey, James, and Merel Kindt. ‘Knowing When to Trust Your Gut: The Perceived Trustworthiness of Fear Varies with Domain Expertise’. PsyArXiv, 16 April 2021. https://doi.org/10.31234/osf.io/682su.
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- Mar 2021
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science.sciencemag.org science.sciencemag.org
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Dehning, J., Zierenberg, J., Spitzner, F. P., Wibral, M., Neto, J. P., Wilczek, M., & Priesemann, V. (2020). Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science. https://doi.org/10.1126/science.abb9789
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www.biorxiv.org www.biorxiv.org
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Bertana, A., Chetverikov, A., Bergen, R. S. van, Ling, S., & Jehee, J. F. M. (2020). Dual strategies in human confidence judgments. BioRxiv, 2020.09.17.299743. https://doi.org/10.1101/2020.09.17.299743
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psyarxiv.com psyarxiv.com
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Gligorić, Vukašin, Allard Feddes, and Bertjan Doosje. ‘Political Bullshit Receptivity and Its Correlates: A Cross-Cultural Validation of the Concept’. PsyArXiv, 27 October 2020. https://doi.org/10.31234/osf.io/u9pe3.
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en.wikipedia.org en.wikipedia.org
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inference and learning in Bayesian networks.
Learning como Machine learning?
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twitter.com twitter.com
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ReconfigBehSci. (2020, October 27). RT @JASPStats: How to perform Robust Bayesian Meta-Analysis in JASP. To learn more, have a look at the tutorial video: Https://t.co/4fmkLEH… [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1321387314887708672
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- Feb 2021
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psyarxiv.com psyarxiv.com
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Perez Santangelo, A., & Solovey, G. (2020, November 9). Time to Shine: Reliable Response-Timing Using R-Shiny for Online Experiments. https://doi.org/10.31234/osf.io/nuxdg
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- Dec 2020
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blogs.bmj.com blogs.bmj.com
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Karl Friston and Anthony Costello: What we have learned from the second covid-19 surge? (2020, December 8). The BMJ. https://blogs.bmj.com/bmj/2020/12/08/karl-friston-and-anthony-costello-what-we-have-learned-from-the-second-covid-19-surge/
Tags
- lang:en
- case number
- is:blog
- inaccuracy
- criticism
- Bayesian
- second wave
- vaccine
- epidemiology
- COVID-19
- modeling
- forecast
- prediction
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URL
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- Oct 2020
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metascience.com metascience.com
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Fiona Fidler: Misinterpretations of evidence, and worse misinterpretations of evidence (Video). (n.d.). Metascience.com. Retrieved 29 October 2020, from https://metascience.com/events/metascience-2019-symposium/fiona-fidler-misinterpretations-of-evidence/
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seeing-theory.brown.edu seeing-theory.brown.edu
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Kunin, D. (n.d.). Seeing Theory. Retrieved October 27, 2020, from http://seeingtheory.io
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- Sep 2020
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ideas.repec.org ideas.repec.org
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Kubinec, Robert & Carvalho, Luiz & Barceló, Joan & Cheng, Cindy & Hartnett, Allison & Messerschmidt, Luca & Duba, Derek & Cottrell, Matthew Sean, 2020. "Partisanship and the Spread of COVID-19 in the United States," SocArXiv jp4wk, Center for Open Science. Retrieved from: https://ideas.repec.org/p/osf/socarx/jp4wk.html
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www.medrxiv.org www.medrxiv.org
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Friston, K., Costello, A., & Pillay, D. (2020). Dark matter, second waves and epidemiological modelling. MedRxiv, 2020.09.01.20185876. https://doi.org/10.1101/2020.09.01.20185876
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www.imperial.ac.uk www.imperial.ac.uk
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Report 13—Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. (n.d.). Imperial College London. Retrieved September 18, 2020, from http://www.imperial.ac.uk/medicine/departments/school-public-health/infectious-disease-epidemiology/mrc-global-infectious-disease-analysis/covid-19/report-13-europe-npi-impact/
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psyarxiv.com psyarxiv.com
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Schnuerch, M., Nadarevic, L., & Rouder, J. (2020). The truth revisited: Bayesian analysis of individual differences in the truth effect [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/nfm6k
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ojs.uwindsor.ca ojs.uwindsor.ca
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Hahn, U., & Oaksford, M. (2006). A Normative Theory of Argument Strength. Informal Logic, 26(1), 1–24. https://doi.org/10.22329/il.v26i1.428
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- Aug 2020
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Laghaie, A., & Otter, T. (2020). Measuring evidence for mediation in the presence of measurement error [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/5bz3f
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www.nber.org www.nber.org
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Chaudhuri, S., Lo, A. W., Xiao, D., & Xu, Q. (2020). Bayesian Adaptive Clinical Trials for Anti‐Infective Therapeutics during Epidemic Outbreaks (Working Paper No. 27175; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27175
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Young, J.-G., Cantwell, G. T., & Newman, M. E. J. (2020). Robust Bayesian inference of network structure from unreliable data. ArXiv:2008.03334 [Physics, Stat]. http://arxiv.org/abs/2008.03334
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Field, S. M., Hoek, J. M., de Vries, Y. A., Linde, M., Pittelkow, M., Muradchanian, J., & van Ravenzwaaij, D. (2020, July 30). Rethinking Remdesivir for COVID-19: A Bayesian Reanalysis of Trial Findings. https://doi.org/10.31222/osf.io/2kam7
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- Jul 2020
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www.nber.org www.nber.org
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Liu, L., Moon, H. R., & Schorfheide, F. (2020). Panel Forecasts of Country-Level Covid-19 Infections (Working Paper No. 27248; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27248
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www.youtube.com www.youtube.com
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MCLS Virtual Brown Bag June 12, 2020: Bayesian Modelling. (2020, June 15). https://www.youtube.com/watch?v=7LLZPNLhn5o
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osf.io osf.io
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Kubinec, R., & Carvalho, L. (2020). A Retrospective Bayesian Model for Measuring Covariate Effects on Observed COVID-19 Test and Case Counts [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/jp4wk
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psyarxiv.com psyarxiv.com
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Stefani, S., & Prati, G. (2020, April 24). Are Dimensions of Gender Inequality Uniformly Associated with Human Values?. https://doi.org/10.31234/osf.io/jacuw
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www.sciencedirect.com www.sciencedirect.com
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Argument Quality in Real World Argumentation. (2020). Trends in Cognitive Sciences, 24(5), 363–374. https://doi.org/10.1016/j.tics.2020.01.004
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jasp-stats.org jasp-stats.org
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Introducing JASP 0.13. (2020, July 2). JASP - Free and User-Friendly Statistical Software. https://jasp-stats.org/?p=6483
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- Jun 2020
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psyarxiv.com psyarxiv.com
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Maier, M., Bartoš, F., & Wagenmakers, E.-J. (2020). Robust Bayesian Meta-Analysis: Addressing Publication Bias with Model-Averaging [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/u4cns
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wellcomeopenresearch.org wellcomeopenresearch.org
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Friston KJ, Parr T, Zeidman P et al. Dynamic causal modelling of COVID-19 [version 1; peer review: awaiting peer review]. Wellcome Open Res 2020, 5:89 (https://doi.org/10.12688/wellcomeopenres.15881.1)
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www.researchgate.net www.researchgate.net
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Mclachlan, S., Lucas, P., Kudakwashe Dube, Hitman, G. A., Osman, M., Kyrimi, E., Neil, M., & Fenton, N. E. (2020). The fundamental limitations of COVID-19 contact tracing methods and how to resolve them with a Bayesian network approach. https://doi.org/10.13140/RG.2.2.27042.66243
Tags
- contact tracing
- lang:en
- Bayesian
- app
- network model
- containment
- COVID-19
- digital solution
- limitation
- prediction
- likelihood
- is:preprint
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arxiv.org arxiv.org
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Friston, K. J., Parr, T., Zeidman, P., Razi, A., Flandin, G., Daunizeau, J., Hulme, O. J., Billig, A. J., Litvak, V., Moran, R. J., Price, C. J., & Lambert, C. (2020). Dynamic causal modelling of COVID-19. ArXiv:2004.04463 [q-Bio]. http://arxiv.org/abs/2004.04463
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psyarxiv.com psyarxiv.com
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McDonald, K., Ding, T., French, P., Jones, P. B., Baio, G., Kirkbride, J. B., & Wohland, P. (2020, April 27). Forecasting population need for mental health care: a Bayesian methodology applied to the epidemiology of psychotic disorders in England. Retrieved from psyarxiv.com/bvcgu
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psyarxiv.com psyarxiv.com
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Han, H., & Dawson, K. J. (2020). JASP (Software) [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/67dcb
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psyarxiv.com psyarxiv.com
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Leplaa, H. J., Rietbergen, C., & Hoijtink, H. (2020). Bayesian evaluation of replication studies [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/49tbz
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- May 2020
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psyarxiv.com psyarxiv.com
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Zmigrod, L., Eisenberg, I. W., Bissett, P., Robbins, T. W., & Poldrack, R. (2020, April 14). A Data-Driven Analysis of the Cognitive and Perceptual Attributes of Ideological Attitudes. https://doi.org/10.31234/osf.io/dgaxr
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psyarxiv.com psyarxiv.com
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Fränken, J.-P., & Pilditch, T. (2020). Cascades across networks are sufficient for the formation of echo chambers: An agent-based model. https://doi.org/10.31234/osf.io/8rgkc
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Ciranka, S. K., & van den Bos, W. (2020). A Bayesian Model of Social Influence under Risk and Uncertainty [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/mujek
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psyarxiv.com psyarxiv.com
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Fischer, H., & Said, N. (2020, May 12). Metacognition_ClimateChange_Fischer&Said_Preprint. https://doi.org/10.31234/osf.io/fd6gy
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psyarxiv.com psyarxiv.com
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Wagenmakers, E., & Gronau, Q. F. (2020, April 27). Efficacy of Hydroxychloroquine in Patients with COVID-19 (Chen et al., 2020): Moderate Evidence for a Treatment Effect on Pneumonia. Retrieved from psyarxiv.com/7nk8z
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www.tandfonline.com www.tandfonline.com
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Fenton, N. E., Neil, M., Osman, M., & McLachlan, S. (2020). COVID-19 infection and death rates: The need to incorporate causal explanations for the data and avoid bias in testing. Journal of Risk Research, 0(0), 1–4. https://doi.org/10.1080/13669877.2020.1756381
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psyarxiv.com psyarxiv.com
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Barnby, J. M., Bell, V., Mehta, M., & Moutoussis, M. (2020, April 17). Reduction in social learning and policy uncertainty about intentional social threat underlies paranoia: evidence from modelling a modified serial dictator game. https://doi.org/10.31234/osf.io/jvx5y
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easystats.github.io easystats.github.io
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psyarxiv.com psyarxiv.com
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Haaf, J. M., Hoogeveen, S., Berkhout, S., Gronau, Q. F., & Wagenmakers, E. (2020, April 14). A Bayesian Multiverse Analysis of Many Labs 4: Quantifying the Evidence against Mortality Salience. https://doi.org/10.31234/osf.io/cb9er
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www.imperial.ac.uk www.imperial.ac.uk
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Seth Flaxman, Swapnil Mishra, Axel Gandy et al. Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. Imperial College London (2020), doi:https://doi.org/10.25561/77731
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Martins, A. C. R. (2020). Extremism definitions in opinion dynamics models. ArXiv:2004.14548 [Nlin, Physics:Physics]. http://arxiv.org/abs/2004.14548
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www.fil.ion.ucl.ac.uk www.fil.ion.ucl.ac.uk
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Friston, K. J., Parr, T., Zeidman, P., Razi, A., Flandin, G., Daunizeau, J., Hulme, O. J., Billig, A. J., Litvak, V., Moran, R. J., Price, C. J., & Lambert, C. (2020). Dynamic causal modelling of COVID-19. ArXiv:2004.04463 [q-Bio]. http://arxiv.org/abs/2004.04463
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psyarxiv.com psyarxiv.com
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Zinn, S., & Gnambs, T. (2020, April 18). Analyzing nonresponse in longitudinal surveys using Bayesian additive regression trees: A nonparametric event history analysis. https://doi.org/10.31234/osf.io/82c3w
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www.thelancet.com www.thelancet.com
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Zhang, J. et al. (2020, April 2). Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study. The Lancet: Infectious Diseases. https://doi.org/10.1016/S1473-3099(20)30230-9.
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github.com github.com
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McElreath, R. Statistical Rethinking: A Bayesian Course Using R and Stan Github.com. https://github.com/rmcelreath/statrethinking_winter2019
Entire course with materials online.
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- Apr 2020
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psyarxiv.com psyarxiv.com
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Derks, K., de swart, j., van Batenburg, P., Wagenmakers, E., & wetzels, r. (2020, April 28). Priors in a Bayesian Audit: How Integration of Existing Information into the Prior Distribution Can Increase Transparency, Efficiency, and Quality. Retrieved from psyarxiv.com/8fhkp
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- Nov 2018
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iphysresearch.github.io iphysresearch.github.io
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Explaining Deep Learning Models - A Bayesian Non-parametric Approach
无疑,讨论模型可解释性的 paper 总是让人充满好奇的。 文中说前人据网络的 output 形成了两种解释思路:whitebox/blackbox explanation。此文提出了新black-box方法(general sensitivity level of a target model to specific input dimensions) 通过建立 DMM-MEN。
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- Sep 2018
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conditional distribution for individual components can be constructed
So the conditional distribution is conditioned on other components?
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p(y∣x)=∫p(y∣f,x)p(f∣x)df
\(y\) is the data, \(f\) is the model, \(x\) is the input variable
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am207.github.io am207.github.io
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in equation B for the marginal of a gaussian, only the covariance of the block of the matrix involving the unmarginalized dimensions matters! Thus “if you ask only for the properties of the function (you are fitting to the data) at a finite number of points, then inference in the Gaussian process will give you the same answer if you ignore the infinitely many other points, as if you would have taken them all into account!”(Rasmunnsen)
key insight into Gaussian processes
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- Jul 2018
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am207.github.io am207.github.io
- Jan 2016
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blogs.scientificamerican.com blogs.scientificamerican.com
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P(B|E) = P(B) X P(E|B) / P(E), with P standing for probability, B for belief and E for evidence. P(B) is the probability that B is true, and P(E) is the probability that E is true. P(B|E) means the probability of B if E is true, and P(E|B) is the probability of E if B is true.
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The probability that a belief is true given new evidence equals the probability that the belief is true regardless of that evidence times the probability that the evidence is true given that the belief is true divided by the probability that the evidence is true regardless of whether the belief is true. Got that?
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Initial belief plus new evidence = new and improved belief.
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- Oct 2015
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Nearly all applications of probability to cryptography depend on the factor principle (or Bayes’ Theorem).
This is easily the most interesting sentence in the paper: Turing used Bayesian analysis for code-breaking during WWII.
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