作者: MORT 時(shí)間: 2025-3-21 22:24
Craniopharyngioma in Pediatrics and Adults,In science, we use models to help us learn from data. But we always work with incomplete theory and measurements that contain errors.作者: Armada 時(shí)間: 2025-3-22 01:01
Angio-architecture of the MedullaAs scientists, we want to know how to parameterise our models, make comparisons with other models, and quantify model predictive uncertainty. For all these purposes, measurement data are needed, but how exactly should we use the data? The answer is always the same: in the ..作者: 點(diǎn)燃 時(shí)間: 2025-3-22 07:01
Angio-architecture of the MedullaThe Bayesian approach to parameter estimation requires modellers to make a major mental shift: we no longer aim to find a single ‘best’ parameter vector—instead we aim to determine the posterior probability distribution for the parameters.作者: orthodox 時(shí)間: 2025-3-22 10:13 作者: Esalate 時(shí)間: 2025-3-22 13:09 作者: 填料 時(shí)間: 2025-3-22 17:18 作者: inventory 時(shí)間: 2025-3-23 01:00 作者: 神圣在玷污 時(shí)間: 2025-3-23 03:19
E. Kenneth Parkinson,W. Andrew YeudallIn this chapter, we discuss how multiple ‘competing’ models can be used simultaneously. There are advantages to having multiple different models, as was already recognized by Chamberlin in the 19th century (Chamberlin 1890).作者: inflate 時(shí)間: 2025-3-23 07:24 作者: thrombus 時(shí)間: 2025-3-23 13:01 作者: DIS 時(shí)間: 2025-3-23 14:50 作者: Conflict 時(shí)間: 2025-3-23 20:33
Introduction to Bayesian Science,In science, we use models to help us learn from data. But we always work with incomplete theory and measurements that contain errors.作者: squander 時(shí)間: 2025-3-23 23:01
Assigning a Likelihood Function,As scientists, we want to know how to parameterise our models, make comparisons with other models, and quantify model predictive uncertainty. For all these purposes, measurement data are needed, but how exactly should we use the data? The answer is always the same: in the ..作者: 頂點(diǎn) 時(shí)間: 2025-3-24 04:51
Sampling from Any Distribution by MCMC,The Bayesian approach to parameter estimation requires modellers to make a major mental shift: we no longer aim to find a single ‘best’ parameter vector—instead we aim to determine the posterior probability distribution for the parameters.作者: FAR 時(shí)間: 2025-3-24 08:59 作者: opinionated 時(shí)間: 2025-3-24 14:10
MCMC and Complex Models,In this chapter we focus on models with multivariate output. That includes most process-based models (PBMs). Models with multivariate output are not fundamentally different from the simpler models we studied in the previous chapters, we can still write them as functions . of their input consisting of covariates . and parameters ..作者: 憂傷 時(shí)間: 2025-3-24 16:18 作者: 頌揚(yáng)國家 時(shí)間: 2025-3-24 20:20
After the Calibration: Interpretation, Reporting, Visualization,This?chapter discusses what needs to be done after your Bayesian calibration: how to interpret your results, what to report and how to report it with emphasis on visualization.作者: avarice 時(shí)間: 2025-3-25 02:46
Model Ensembles: BMC and BMA,In this chapter, we discuss how multiple ‘competing’ models can be used simultaneously. There are advantages to having multiple different models, as was already recognized by Chamberlin in the 19th century (Chamberlin 1890).作者: Airtight 時(shí)間: 2025-3-25 06:49 作者: LUCY 時(shí)間: 2025-3-25 10:54 作者: 蒸發(fā) 時(shí)間: 2025-3-25 12:32 作者: 阻塞 時(shí)間: 2025-3-25 16:55 作者: Noisome 時(shí)間: 2025-3-25 21:35 作者: artless 時(shí)間: 2025-3-26 02:15 作者: tenuous 時(shí)間: 2025-3-26 08:14 作者: jovial 時(shí)間: 2025-3-26 08:56
Angio-architecture of the Medullaowledge, and whatever the subject is, people have different knowledge and expertise. So instead of speaking of “the prior probability of .”, each of us should say “my prior probability for .”. We . a prior probability distribution, we do not . it. This is even the case when we invite the opinion of 作者: irreducible 時(shí)間: 2025-3-26 12:57
https://doi.org/10.1007/978-3-662-02299-3es the information content of our data. So all that is left, is to apply Bayes’ Theorem (Eq. (.)) to derive our desired posterior distribution. Note that when talking about the posterior, we use the phrase ‘deriving the’ distribution rather than ‘a(chǎn)ssigning a’ distribution.作者: Plaque 時(shí)間: 2025-3-26 19:42
https://doi.org/10.1007/978-1-349-18312-8ame mathematics. In this chapter, we shall fit a line to data in twelve different ways and compare the resulting parameter estimates. So our goal is to estimate the intercept and the slope of the line.作者: 圣人 時(shí)間: 2025-3-26 23:23
Christos Paraskeva,Angela Hagueertainty translates into predictive uncertainty. And if we get new data, then we can use Bayes’ Theorem to update the parameter distribution and thereby reduce our predictive uncertainty. But a more difficult problem is that of uncertainty about model structure. We know that all models are wrong, bu作者: 約會 時(shí)間: 2025-3-27 03:54
https://doi.org/10.1007/978-90-481-8537-5 information about the nodes. So the graph is just the visible part of the model. GMs do not represent a new kind of statistical model, they are just helpful tools for analysing joint probability distributions. Every distribution can be represented by a GM, so whatever your research problem or model作者: wall-stress 時(shí)間: 2025-3-27 06:47
Human Capacities and Moral Statusction ., and that was it. Bayes’ theorem then told us what the posterior distribution would be once we received the data: .. The prior for the parameter vector was always a fully specified distribution, e.g.?the product of known univariate Gaussians. In hierarchical modelling, we do not specify the 作者: 用不完 時(shí)間: 2025-3-27 12:27
Productiveness of Welfare Expenditures, preceding chapters, this approach allows us to quantify predictive uncertainty when using our models to predict the future. And this is of course important for the user of these predictions, whether that user is us or someone whom we report our results to. Our probabilistic results allow not just p作者: 隼鷹 時(shí)間: 2025-3-27 15:00 作者: STIT 時(shí)間: 2025-3-27 18:05
Marcel van OijenShows how Bayesian algorithms work in an easy to understand way.Explains Markov Chain Monte Carlo sampling with straightforward examples.Complemented with the R codes used in the book for modelling, d作者: A精確的 時(shí)間: 2025-3-27 22:14 作者: 同位素 時(shí)間: 2025-3-28 05:39 作者: conquer 時(shí)間: 2025-3-28 06:36 作者: 博愛家 時(shí)間: 2025-3-28 12:25 作者: CLOUT 時(shí)間: 2025-3-28 14:41 作者: 主講人 時(shí)間: 2025-3-28 20:43
Angio-architecture of the Medullas should say “my prior probability for .”. We . a prior probability distribution, we do not . it. This is even the case when we invite the opinion of experts on the likely values of our model’s?parameters.作者: TIA742 時(shí)間: 2025-3-28 23:41
https://doi.org/10.1007/978-90-481-8537-5helpful tools for analysing joint probability distributions. Every distribution can be represented by a GM, so whatever your research problem or modelling method is, you can choose to use a GM to organize your thinking.作者: crutch 時(shí)間: 2025-3-29 06:01
Human Capacities and Moral Statuser vector was always a fully specified distribution, e.g.?the product of known univariate Gaussians. In hierarchical modelling, we do not specify the prior that directly. Instead we make the prior distribution depend on other parameters, which we call hyperparameters.作者: 名次后綴 時(shí)間: 2025-3-29 10:49 作者: Omniscient 時(shí)間: 2025-3-29 12:15
Custom and Path Dependence in Economics,ribution may require computationally demanding methods such as MCMC. So people keep searching for shortcuts where the Bayesian analysis can be made faster albeit perhaps a little bit less informative and accurate.作者: Genistein 時(shí)間: 2025-3-29 19:17
Assigning a Prior Distribution,s should say “my prior probability for .”. We . a prior probability distribution, we do not . it. This is even the case when we invite the opinion of experts on the likely values of our model’s?parameters.作者: LAVA 時(shí)間: 2025-3-29 21:17
Graphical Modelling (GM),helpful tools for analysing joint probability distributions. Every distribution can be represented by a GM, so whatever your research problem or modelling method is, you can choose to use a GM to organize your thinking.作者: 協(xié)奏曲 時(shí)間: 2025-3-30 01:56 作者: placebo-effect 時(shí)間: 2025-3-30 05:07
Probabilistic Risk Analysis and Bayesian Decision Theory,ortant for the user of these predictions, whether that user is us or someone whom we report our results to. Our probabilistic results allow not just prediction but also calculation of risks and, more generally, support for decision-making.作者: 性冷淡 時(shí)間: 2025-3-30 11:42
Approximations to Bayes,ribution may require computationally demanding methods such as MCMC. So people keep searching for shortcuts where the Bayesian analysis can be made faster albeit perhaps a little bit less informative and accurate.作者: 重疊 時(shí)間: 2025-3-30 14:33 作者: GET 時(shí)間: 2025-3-30 17:09 作者: Indecisive 時(shí)間: 2025-3-30 21:31 作者: 總 時(shí)間: 2025-3-31 03:26
Assigning a Prior Distribution,owledge, and whatever the subject is, people have different knowledge and expertise. So instead of speaking of “the prior probability of .”, each of us should say “my prior probability for .”. We . a prior probability distribution, we do not . it. This is even the case when we invite the opinion of 作者: 脖子 時(shí)間: 2025-3-31 06:23 作者: SNEER 時(shí)間: 2025-3-31 13:02
Twelve Ways to Fit a Straight Line,ame mathematics. In this chapter, we shall fit a line to data in twelve different ways and compare the resulting parameter estimates. So our goal is to estimate the intercept and the slope of the line.作者: 全神貫注于 時(shí)間: 2025-3-31 16:31 作者: magnanimity 時(shí)間: 2025-3-31 20:42
Graphical Modelling (GM), information about the nodes. So the graph is just the visible part of the model. GMs do not represent a new kind of statistical model, they are just helpful tools for analysing joint probability distributions. Every distribution can be represented by a GM, so whatever your research problem or model作者: Nucleate 時(shí)間: 2025-3-31 22:12
Bayesian Hierarchical Modelling (BHM),ction ., and that was it. Bayes’ theorem then told us what the posterior distribution would be once we received the data: .. The prior for the parameter vector was always a fully specified distribution, e.g.?the product of known univariate Gaussians. In hierarchical modelling, we do not specify the 作者: 諂媚于性 時(shí)間: 2025-4-1 05:01 作者: Hay-Fever 時(shí)間: 2025-4-1 09:57
Approximations to Bayes,ata likelihood function. But we have also seen that assigning a prior and likelihood is not always easy, and deriving a sample from the posterior distribution may require computationally demanding methods such as MCMC. So people keep searching for shortcuts where the Bayesian analysis can be made fa作者: START 時(shí)間: 2025-4-1 10:43
