#bayesian — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #bayesian, aggregated by home.social.
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New in Demographic Research: Our Bayesian multi-dimensional mortality reconstruction integrates Eurostat, DHS, UN WPP & WIC data to flexibly estimate age-, sex-, and education-specific mortality across countries. #Bayesian #Demography #popjus #iiasa #WIC
https://www.demographic-research.org/articles/volume/54/28 -
Embracing Bayesian methods in clinical trials
https://jamanetwork.com/journals/jama/fullarticle/2847011
#HackerNews #Embracing #Bayesian #methods #in #clinical #trials #BayesianMethods #ClinicalTrials #DataScience #HealthResearch #JAMA
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Embracing Bayesian methods in clinical trials
https://jamanetwork.com/journals/jama/fullarticle/2847011
#HackerNews #Embracing #Bayesian #methods #in #clinical #trials #BayesianMethods #ClinicalTrials #DataScience #HealthResearch #JAMA
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Embracing Bayesian methods in clinical trials
https://jamanetwork.com/journals/jama/fullarticle/2847011
#HackerNews #Embracing #Bayesian #methods #in #clinical #trials #BayesianMethods #ClinicalTrials #DataScience #HealthResearch #JAMA
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Embracing Bayesian methods in clinical trials
https://jamanetwork.com/journals/jama/fullarticle/2847011
#HackerNews #Embracing #Bayesian #methods #in #clinical #trials #BayesianMethods #ClinicalTrials #DataScience #HealthResearch #JAMA
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Embracing Bayesian methods in clinical trials
https://jamanetwork.com/journals/jama/fullarticle/2847011
#HackerNews #Embracing #Bayesian #methods #in #clinical #trials #BayesianMethods #ClinicalTrials #DataScience #HealthResearch #JAMA
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"Five skills. Each one is counter-cyclical (becomes more valuable as hype recedes), resistant to LLM automation (requires human judgment that pattern-matching can’t replicate), and directly tied to the business outcomes executives actually pay for."
by Kaushik Rajan: https://towardsdatascience.com/the-ai-bubble-has-a-data-science-escape-hatch/#DataScience #BayesianStatistics #BayesianStats #Bayesian #causalInference #experimentalDesign #SPC #statisticalProcessControl
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Stuart McAlpine - Creating a Bayesian digital twin of our Universe (Sept. 18, 2025) https://www.youtube.com/watch?v=q5eSO_YOZR8
The Manticore Project I: a digital twin of our cosmic neighbourhood from Bayesian field-level analysis
https://arxiv.org/abs/2505.10682"Leveraging field-level Bayesian inference with data from the
2M++ galaxy survey, our model incorporates refined galaxy bias modelling, high-fidelity reconstructions, and rigorous posterior predictive validation. As a result, Manticore-Local accurately reproduces the spatial distribution, mass hierarchy, and velocity fields of observed cosmic structures within the local Universe, outperforming previous state-of-the-art constrained simulations and velocity field reconstructions across multiple key metrics." -
Stuart McAlpine - Creating a Bayesian digital twin of our Universe (Sept. 18, 2025) https://www.youtube.com/watch?v=q5eSO_YOZR8
The Manticore Project I: a digital twin of our cosmic neighbourhood from Bayesian field-level analysis
https://arxiv.org/abs/2505.10682"Leveraging field-level Bayesian inference with data from the
2M++ galaxy survey, our model incorporates refined galaxy bias modelling, high-fidelity reconstructions, and rigorous posterior predictive validation. As a result, Manticore-Local accurately reproduces the spatial distribution, mass hierarchy, and velocity fields of observed cosmic structures within the local Universe, outperforming previous state-of-the-art constrained simulations and velocity field reconstructions across multiple key metrics." -
Stuart McAlpine - Creating a Bayesian digital twin of our Universe (Sept. 18, 2025) https://www.youtube.com/watch?v=q5eSO_YOZR8
The Manticore Project I: a digital twin of our cosmic neighbourhood from Bayesian field-level analysis
https://arxiv.org/abs/2505.10682"Leveraging field-level Bayesian inference with data from the
2M++ galaxy survey, our model incorporates refined galaxy bias modelling, high-fidelity reconstructions, and rigorous posterior predictive validation. As a result, Manticore-Local accurately reproduces the spatial distribution, mass hierarchy, and velocity fields of observed cosmic structures within the local Universe, outperforming previous state-of-the-art constrained simulations and velocity field reconstructions across multiple key metrics." -
Stuart McAlpine - Creating a Bayesian digital twin of our Universe (Sept. 18, 2025) https://www.youtube.com/watch?v=q5eSO_YOZR8
The Manticore Project I: a digital twin of our cosmic neighbourhood from Bayesian field-level analysis
https://arxiv.org/abs/2505.10682"Leveraging field-level Bayesian inference with data from the
2M++ galaxy survey, our model incorporates refined galaxy bias modelling, high-fidelity reconstructions, and rigorous posterior predictive validation. As a result, Manticore-Local accurately reproduces the spatial distribution, mass hierarchy, and velocity fields of observed cosmic structures within the local Universe, outperforming previous state-of-the-art constrained simulations and velocity field reconstructions across multiple key metrics." -
Stuart McAlpine - Creating a Bayesian digital twin of our Universe (Sept. 18, 2025) https://www.youtube.com/watch?v=q5eSO_YOZR8
The Manticore Project I: a digital twin of our cosmic neighbourhood from Bayesian field-level analysis
https://arxiv.org/abs/2505.10682"Leveraging field-level Bayesian inference with data from the
2M++ galaxy survey, our model incorporates refined galaxy bias modelling, high-fidelity reconstructions, and rigorous posterior predictive validation. As a result, Manticore-Local accurately reproduces the spatial distribution, mass hierarchy, and velocity fields of observed cosmic structures within the local Universe, outperforming previous state-of-the-art constrained simulations and velocity field reconstructions across multiple key metrics." -
I have an extremely naive question about probability. Suppose I have a possibly biased d6 and a roll it a bunch, and get a sample pmf
\[p_1\colon \underline{6}=\{1,2,3,4,5,6\} \to [0,1].\]
Then I give it to you and you do the same to get \(p_2\) based purely on your rolls. Then in a fit of madness we calculate the function \(p\colon \underline{6}\to [0,1]\) defined by
\[ p(x) = \frac{p_1(x)p_2(x)}{\sum_y p_1(y)p_2(y)},\]
which is also a pmf.
Does this represent anything? Something like a pooling of our information? A kind of update procedure? Something that violates dimensional analysis? Is it just junk?A more sophisticated analysis might do something like assume we have a true distribution (remember: possibly biased) and see if the pmf \(p\) is "better" or not compared with \(p_1\) and \(p_2\). A more computational person might even simulate this. But what if we didn't know a true distribution here? Then it's more or less just multiplying a pair of what amount to priors on the same sample space. If we lost or forgot the data of the actual number of rolls, and only remembered the pmfs, we wouldn't be able to correctly combine our rolling experiments to get a single empirical pmf to compare the individual ones.
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CW: Long HiveMind request: Guidelines for causal DAGs in Bayesian modelling
Fediverse #HiveMind request: Guidelines for causal DAGs in Bayesian modelling
One of my many personal peeves is sloppy conceptual diagramming. I have been exposed to far too many box and arrow diagrams where there is no clear and consistent interpretation of what types of things the boxes are supposed to represent (and ditto for the arrows).
I am starting to engage with a Bayesian modelling project (this I know from nothing - https://tomlehrersongs.com/wp-content/uploads/2018/12/lobachevsky.pdf) and am being exposed to causal DAGs. I presume these are reasonably consistent and interpretable. Nonetheless, they are typically presented as though the interpretation is self-evident and I keep coming up with multiple possible interpretations/constraints.
>>>>> Do people have recommendations for *detailed* interpretations of causal DAGs as used in Bayesian modelling? <<<<<
Issues covered should include things like:
* Constraints on node types: If nodes represent variables can they have multivariate values or are they constrained to be univariate?
* Interpretation with respect to "cases": Should the DAG be interpreted as referring to relationships that hold on a per-case basis?
* Interpretation with respect to "populations": If DAGs represent per-case relations, do they also represent populatiopns of those cases?
* Interpretation with respect to data matrices: How does a causal DAG map onto a matrix of data to be modelled?
* Interpretation with respect to multiple types of "case": Repeat all of the issues above but when there are multiple types of case not necessarily in one-to-one relationships (e.g. multi-level modelling).
* What is the relationship between the DAG and the equations/statements implementing that DAG in the probabilistic programming language of choice (e.g. STAN, PyMC)?
* What are the constraints on the temporal relationships of the variables at the nodes? Assuming the variables are measures at points in time you (presumably) don't want causes coming after their effects. Different variables may be measured at different points in time and a measurement at some point in time may be a measure of some cumulative process over prior times - so I would expect some nuanced consideration of temporal constraints.#Bayesian #modelling #statistics #causal #DAG #DirectedAcylicGraph #diagram #interpretation
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CW: Long HiveMind request: Guidelines for causal DAGs in Bayesian modelling
Fediverse #HiveMind request: Guidelines for causal DAGs in Bayesian modelling
One of my many personal peeves is sloppy conceptual diagramming. I have been exposed to far too many box and arrow diagrams where there is no clear and consistent interpretation of what types of things the boxes are supposed to represent (and ditto for the arrows).
I am starting to engage with a Bayesian modelling project (this I know from nothing - https://tomlehrersongs.com/wp-content/uploads/2018/12/lobachevsky.pdf) and am being exposed to causal DAGs. I presume these are reasonably consistent and interpretable. Nonetheless, they are typically presented as though the interpretation is self-evident and I keep coming up with multiple possible interpretations/constraints.
>>>>> Do people have recommendations for *detailed* interpretations of causal DAGs as used in Bayesian modelling? <<<<<
Issues covered should include things like:
* Constraints on node types: If nodes represent variables can they have multivariate values or are they constrained to be univariate?
* Interpretation with respect to "cases": Should the DAG be interpreted as referring to relationships that hold on a per-case basis?
* Interpretation with respect to "populations": If DAGs represent per-case relations, do they also represent populatiopns of those cases?
* Interpretation with respect to data matrices: How does a causal DAG map onto a matrix of data to be modelled?
* Interpretation with respect to multiple types of "case": Repeat all of the issues above but when there are multiple types of case not necessarily in one-to-one relationships (e.g. multi-level modelling).
* What is the relationship between the DAG and the equations/statements implementing that DAG in the probabilistic programming language of choice (e.g. STAN, PyMC)?
* What are the constraints on the temporal relationships of the variables at the nodes? Assuming the variables are measures at points in time you (presumably) don't want causes coming after their effects. Different variables may be measured at different points in time and a measurement at some point in time may be a measure of some cumulative process over prior times - so I would expect some nuanced consideration of temporal constraints.#Bayesian #modelling #statistics #causal #DAG #DirectedAcylicGraph #diagram #interpretation
-
CW: Long HiveMind request: Guidelines for causal DAGs in Bayesian modelling
Fediverse #HiveMind request: Guidelines for causal DAGs in Bayesian modelling
One of my many personal peeves is sloppy conceptual diagramming. I have been exposed to far too many box and arrow diagrams where there is no clear and consistent interpretation of what types of things the boxes are supposed to represent (and ditto for the arrows).
I am starting to engage with a Bayesian modelling project (this I know from nothing - https://tomlehrersongs.com/wp-content/uploads/2018/12/lobachevsky.pdf) and am being exposed to causal DAGs. I presume these are reasonably consistent and interpretable. Nonetheless, they are typically presented as though the interpretation is self-evident and I keep coming up with multiple possible interpretations/constraints.
>>>>> Do people have recommendations for *detailed* interpretations of causal DAGs as used in Bayesian modelling? <<<<<
Issues covered should include things like:
* Constraints on node types: If nodes represent variables can they have multivariate values or are they constrained to be univariate?
* Interpretation with respect to "cases": Should the DAG be interpreted as referring to relationships that hold on a per-case basis?
* Interpretation with respect to "populations": If DAGs represent per-case relations, do they also represent populatiopns of those cases?
* Interpretation with respect to data matrices: How does a causal DAG map onto a matrix of data to be modelled?
* Interpretation with respect to multiple types of "case": Repeat all of the issues above but when there are multiple types of case not necessarily in one-to-one relationships (e.g. multi-level modelling).
* What is the relationship between the DAG and the equations/statements implementing that DAG in the probabilistic programming language of choice (e.g. STAN, PyMC)?
* What are the constraints on the temporal relationships of the variables at the nodes? Assuming the variables are measures at points in time you (presumably) don't want causes coming after their effects. Different variables may be measured at different points in time and a measurement at some point in time may be a measure of some cumulative process over prior times - so I would expect some nuanced consideration of temporal constraints.#Bayesian #modelling #statistics #causal #DAG #DirectedAcylicGraph #diagram #interpretation
-
CW: Long HiveMind request: Guidelines for causal DAGs in Bayesian modelling
Fediverse #HiveMind request: Guidelines for causal DAGs in Bayesian modelling
One of my many personal peeves is sloppy conceptual diagramming. I have been exposed to far too many box and arrow diagrams where there is no clear and consistent interpretation of what types of things the boxes are supposed to represent (and ditto for the arrows).
I am starting to engage with a Bayesian modelling project (this I know from nothing - https://tomlehrersongs.com/wp-content/uploads/2018/12/lobachevsky.pdf) and am being exposed to causal DAGs. I presume these are reasonably consistent and interpretable. Nonetheless, they are typically presented as though the interpretation is self-evident and I keep coming up with multiple possible interpretations/constraints.
>>>>> Do people have recommendations for *detailed* interpretations of causal DAGs as used in Bayesian modelling? <<<<<
Issues covered should include things like:
* Constraints on node types: If nodes represent variables can they have multivariate values or are they constrained to be univariate?
* Interpretation with respect to "cases": Should the DAG be interpreted as referring to relationships that hold on a per-case basis?
* Interpretation with respect to "populations": If DAGs represent per-case relations, do they also represent populatiopns of those cases?
* Interpretation with respect to data matrices: How does a causal DAG map onto a matrix of data to be modelled?
* Interpretation with respect to multiple types of "case": Repeat all of the issues above but when there are multiple types of case not necessarily in one-to-one relationships (e.g. multi-level modelling).
* What is the relationship between the DAG and the equations/statements implementing that DAG in the probabilistic programming language of choice (e.g. STAN, PyMC)?
* What are the constraints on the temporal relationships of the variables at the nodes? Assuming the variables are measures at points in time you (presumably) don't want causes coming after their effects. Different variables may be measured at different points in time and a measurement at some point in time may be a measure of some cumulative process over prior times - so I would expect some nuanced consideration of temporal constraints.#Bayesian #modelling #statistics #causal #DAG #DirectedAcylicGraph #diagram #interpretation
-
CW: Long HiveMind request: Guidelines for causal DAGs in Bayesian modelling
Fediverse #HiveMind request: Guidelines for causal DAGs in Bayesian modelling
One of my many personal peeves is sloppy conceptual diagramming. I have been exposed to far too many box and arrow diagrams where there is no clear and consistent interpretation of what types of things the boxes are supposed to represent (and ditto for the arrows).
I am starting to engage with a Bayesian modelling project (this I know from nothing - https://tomlehrersongs.com/wp-content/uploads/2018/12/lobachevsky.pdf) and am being exposed to causal DAGs. I presume these are reasonably consistent and interpretable. Nonetheless, they are typically presented as though the interpretation is self-evident and I keep coming up with multiple possible interpretations/constraints.
>>>>> Do people have recommendations for *detailed* interpretations of causal DAGs as used in Bayesian modelling? <<<<<
Issues covered should include things like:
* Constraints on node types: If nodes represent variables can they have multivariate values or are they constrained to be univariate?
* Interpretation with respect to "cases": Should the DAG be interpreted as referring to relationships that hold on a per-case basis?
* Interpretation with respect to "populations": If DAGs represent per-case relations, do they also represent populatiopns of those cases?
* Interpretation with respect to data matrices: How does a causal DAG map onto a matrix of data to be modelled?
* Interpretation with respect to multiple types of "case": Repeat all of the issues above but when there are multiple types of case not necessarily in one-to-one relationships (e.g. multi-level modelling).
* What is the relationship between the DAG and the equations/statements implementing that DAG in the probabilistic programming language of choice (e.g. STAN, PyMC)?
* What are the constraints on the temporal relationships of the variables at the nodes? Assuming the variables are measures at points in time you (presumably) don't want causes coming after their effects. Different variables may be measured at different points in time and a measurement at some point in time may be a measure of some cumulative process over prior times - so I would expect some nuanced consideration of temporal constraints.#Bayesian #modelling #statistics #causal #DAG #DirectedAcylicGraph #diagram #interpretation
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https://aeon.co/essays/no-schrodingers-cat-is-not-alive-and-dead-at-the-same-time
This is a pretty good article for showing how confused the interpretation of QM is. And its a good article to understand why i personally side with Bohm and Bell in thinking the pilot wave theory is the one most reasonable to believe. Because the pilot wave theory has the following quality. The theory is a mapping from initial position at time t=0 to final position at time t=1...Its deterministic, but our knowledge of the initial condition is not
#quantum #bohm #bayesian -
Environmental & Anthropogenic Influences On Fire Patterns In Tropical Dry Deciduous Forests
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https://doi.org/10.1038/s41598-025-98051-7 <-- shared paper
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#GIS #spatial #mapping #wildfire #fire #busgfire #nvironmental #anthropogenic #influences #humanimpacts #deciduous #forests #firepatterns #spatialanalysis #tropical #ecosystems #management #tree #vegetation #spatiotemporal #hotspots #india #Satpura #tiger #reserve #ecogeography #geography #factors #parameters #drivers #temperature #precipitation #rainfall #model #modeling #bayesian #framework #slope #water #hydrology #infrastructure #mitigation #monitoring #preparedness #forestfires -
'DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning', by Xiangdong Xie, Jiahua Guo, Yi Sun.
http://jmlr.org/papers/v26/23-0002.html
#inference #causal #bayesian -
'DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning', by Xiangdong Xie, Jiahua Guo, Yi Sun.
http://jmlr.org/papers/v26/23-0002.html
#inference #causal #bayesian -
'DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning', by Xiangdong Xie, Jiahua Guo, Yi Sun.
http://jmlr.org/papers/v26/23-0002.html
#inference #causal #bayesian -
'DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning', by Xiangdong Xie, Jiahua Guo, Yi Sun.
http://jmlr.org/papers/v26/23-0002.html
#inference #causal #bayesian -
'DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning', by Xiangdong Xie, Jiahua Guo, Yi Sun.
http://jmlr.org/papers/v26/23-0002.html
#inference #causal #bayesian -
Hot off the press - our report on gender disparities in grant seeking at the University of Cambridge (who applies for and who gets research grant funding).
The story:
1) The structural disparities are big (not so surprising)
2) The patterns of disparity at particular grades in particular disciplines go both ways (more surprising)#ResearchPolicy
#ResearchGrants
#GenderDifferences
#BayesianIf you like graphs, you'll probably like it!
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Immune escape of Hepatitis B virus (HBV): based on >500 HBV genomes and clinical data we could discover many mutations by which the virus can escape immune recognition. We also show how the adaptation of HBV to the immune system changes over the course of the infection. The key tool in this collaboration of virologists and bioinformaticians was our #Bayesian HAMdetector method.
https://doi.org/10.1016/j.jhep.2024.10.047
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Immune escape of Hepatitis B virus (HBV): based on >500 HBV genomes and clinical data we could discover many mutations by which the virus can escape immune recognition. We also show how the adaptation of HBV to the immune system changes over the course of the infection. The key tool in this collaboration of virologists and bioinformaticians was our #Bayesian HAMdetector method.
https://doi.org/10.1016/j.jhep.2024.10.047
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Immune escape of Hepatitis B virus (HBV): based on >500 HBV genomes and clinical data we could discover many mutations by which the virus can escape immune recognition. We also show how the adaptation of HBV to the immune system changes over the course of the infection. The key tool in this collaboration of virologists and bioinformaticians was our #Bayesian HAMdetector method.
https://doi.org/10.1016/j.jhep.2024.10.047
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Immune escape of Hepatitis B virus (HBV): based on >500 HBV genomes and clinical data we could discover many mutations by which the virus can escape immune recognition. We also show how the adaptation of HBV to the immune system changes over the course of the infection. The key tool in this collaboration of virologists and bioinformaticians was our #Bayesian HAMdetector method.
https://doi.org/10.1016/j.jhep.2024.10.047
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Immune escape of Hepatitis B virus (HBV): based on >500 HBV genomes and clinical data we could discover many mutations by which the virus can escape immune recognition. We also show how the adaptation of HBV to the immune system changes over the course of the infection. The key tool in this collaboration of virologists and bioinformaticians was our #Bayesian HAMdetector method.
https://doi.org/10.1016/j.jhep.2024.10.047
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we get this media coverage nonsense when a billionaire's yacht capsizes, but not when boats with hundreds of refugees does the same? :blobPikaUnamused:
billionares should not exist. better for people, animals, planet. everyone...except billionaires.
#eatTheBillionaires #superyacht #bayesian #EqualityMatters -
Sicily yacht sinking: Morgan Stanley International chair Jonathan Bloomer among missing
Morgan Stanley International chairman #Jonathan #Bloomer is among those missing
after a yacht carrying UK tech entrepreneur #Mike #Lynch sank off the coast of Sicily during a violent storm, an Italian official has said.Salvatore Cocina, head of the civil protection agency in #Sicily, said Bloomer and #Chris #Morvillo, a lawyer at Clifford Chance, were among the six people missing.
Lynch and his 18-year-old daughter, #Hannah, were also unaccounted for as of late Monday.The update came as it was reported that Lynch’s 🔸co-defendant in a US trial related to the sale of his software company to Hewlett-Packard had 🔸died after being hit by a car in England.
The British-flagged #Bayesian,
a 56-metre sailboat, was carrying 22 people and anchored just off shore near the port of Porticello when it was hit by a tornado in the early hours of Monday morningOne man, understood to be the vessel’s chef, was confirmed dead. The coastguard said the missing had British, American and Canadian nationalities.
Fifteen people were rescued, including Lynch’s wife, #Angela #Bacares, who owned the boat, and a one-year-old girl who was saved by her mother.
A spokesperson for Lynch, the co-founder of #Autonomy, a software firm that became one of the shining lights of the UK tech scene, declined to comment.
Survivors said the trip had been organised by Lynch for his work colleagues.
Once described as Britain’s Bill Gates, Lynch spent much of the last decade in court defending his name against allegations of fraud related to the sale of his software firm, Autonomy, to the US tech company Hewlett-Packard for $11bn. -
It's that time of the year again...
Registration is open for our summer school in Bayesian methods in health economics - in Florence (just saying...). 22-26 July, this year with a slightly changed team. Check all details here:
https://gianluca.statistica.it/teaching/summer-school/Registration is open here:
http://tinyurl.com/bmhe-24First-come/first-serve, so hurry up!
#costeffectiveness #bayesian #statistics #hta #modelling #BUGS #mcmc
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This festive season 🎄 give the gift 🎁 of a #Bayesian model to a friend still trying to run a frequentist linkage mapping approach looking for significant associations 🧬🖥️
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2 rather informal definitions have been offered:
• 1 Knill & Pouget 2004
“Bayesian coding hypothesis:
Brain represents sensory information probabilistically, in the form of probability distributions”• 2 Friston 2012
“Bayesian brain says that we are trying to infer the causes of our sensations based on a generative model of the world.”Neither even mentions #Bayesian #computations
What then is exactly meant by #BayesianBrain ?
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Scientists Combine Climate Models For More Accurate Projections
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https://phys.org/news/2023-11-scientists-combine-climate-accurate.html <-- shared technical article
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https://www.nature.com/articles/s43247-023-01009-8 <-- shared paper
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This new method provides a framework for how to best understand a collection of climate models. The model weights included in this research informed the Fifth National Climate Assessment, a report released on Nov. 14 that gauges the impacts of climate change in the United States. This project also supports the Earth System Grid Federation, an international collaboration led in the U.S. by DOE that manages and provides access to climate models and observed data…”
#GIS #spatial #mapping #climatechange #spatialanalysis #spatiotemporal #model #modeling #numericmodeling #global #statistics #weighting #bayesian #modelaverging #climatesensivity #climatemodels #projection #ECS #earthsystem #ORNL -
#preprint We have a new #ABM called NormAN (for ‘Normative Argument Exchange Across Networks’) which we hope will help build bridges between research on #OpinionDynamics and #Argumentation research.
It captures the exchange of arguments by #Bayesian agents in a ground truth world based on a #CausalGraph. Code is #Netlogo with #R (netlogo python coming soon).
Take it for a spin!
https://arxiv.org/abs/2311.09254
#Complexity #Argument #ComputationalSocialScience #SocialEpistemology
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I added a small contribution to the density plot debate to the analysis of my crossword solving times: https://jdonland.github.io/posts/crossword_times/crossword_times.html
tl;dr: LOESS-based density plots like the ones produced by `geom_violin` have some problems that are ultimately the result of neglecting to specify and fit your own model.
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@krysdolega A #Bayesian or a #Pragmaticist will agree to the position of #Neurath as stated by #Zolo. I often see this such criticism needed when sociology and #psychology turn to a pseudo-objectivism of the #Fisherian, #frequentist, kind.
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Testing hashtag #MAGA "fields" (AND/0R, #Bayesian, Hidden Markov Modeling #HMM) #covid #microbial #biochemical #metabalomic #connectomics fuel cell https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138448
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Bayesian Optimization with Informative Covariance
Afonso Eduardo, Michael U. Gutmann
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date: 2023-03-30 12:11:48
by: AICareer (🚀Jobs-Internships-Scholarships)PhD Position on Bayesian ML for Wireless Communication
at Eindhoven University of Technology (TU/e)
Check the details here: https://ai-jobs.org/job/phd-position-on-bayesian-machine-learning-for-wireless-communication/#phd #phdscholarship #phdposition #bayesian #machinelearning #ai #artificialintelliegence #wirelesscommunication #technology #ml
🐦🔗: https://twitter.com/twitter/statuses/1641412910030245888
#PhdPosition -
Bayesian Gaussian Mixture Models are a great way to improve the clustering performance of real world datasets.
Find out why and where you might want to use them, and how to implement them efficiently.:
#clustering #DataScience #MachineLearning #Bayesian #Gaussian #MixtureModels #BGMM #GMM #unsupervisedlearning
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#DavidKipping and #LexFridman - Do we live in a #Simulation?
https://www.youtube.com/watch?v=u201Bb0ev88&ab_channel=LexClips
#Science #Philosophy #Logic #Statistics #Probability #Bayesian #BayesianPrior #BayesianPriors #BayesianStatistics #SimulationHypothesis #TheSimulationHypothesis #AncestorSimulation #Universe #SimulatedUniverse #Bostrom #NickBostrom
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"In a #Bayesian model, a likelihood is a #prior for the data, and inference about parameters can be surprisingly insensitive to its details." from @rlmcelreath book "Statistical Rethinking"
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"This review summarizes the current state of knowledge on Bayes' theorem, and its potential applications to the field of neurocritical care"
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"This review summarizes the current state of knowledge on Bayes' theorem, and its potential applications to the field of neurocritical care"
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"This review summarizes the current state of knowledge on Bayes' theorem, and its potential applications to the field of neurocritical care"
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"This review summarizes the current state of knowledge on Bayes' theorem, and its potential applications to the field of neurocritical care"