nhaliday + latent-variables   38

Fitting a Structural Equation Model
seems rather unrigorous: nonlinear optimization, possibility of nonconvergence, doesn't even mention local vs. global optimality...
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november 2017 by nhaliday
Stat 260/CS 294: Bayesian Modeling and Inference
Topics
- Priors (conjugate, noninformative, reference)
- Hierarchical models, spatial models, longitudinal models, dynamic models, survival models
- Testing
- Model choice
- Inference (importance sampling, MCMC, sequential Monte Carlo)
- Nonparametric models (Dirichlet processes, Gaussian processes, neutral-to-the-right processes, completely random measures)
- Decision theory and frequentist perspectives (complete class theorems, consistency, empirical Bayes)
- Experimental design
unit  course  berkeley  expert  michael-jordan  machine-learning  acm  bayesian  probability  stats  lecture-notes  priors-posteriors  markov  monte-carlo  frequentist  latent-variables  decision-theory  expert-experience  confidence  sampling 
july 2017 by nhaliday
Unsupervised learning, one notion or many? – Off the convex path
(Task A) Learning a distribution from samples. (Examples: gaussian mixtures, topic models, variational autoencoders,..)

(Task B) Understanding latent structure in the data. This is not the same as (a); for example principal component analysis, clustering, manifold learning etc. identify latent structure but don’t learn a distribution per se.

(Task C) Feature Learning. Learn a mapping from datapoint → feature vector such that classification tasks are easier to carry out on feature vectors rather than datapoints. For example, unsupervised feature learning could help lower the amount of labeled samples needed for learning a classifier, or be useful for domain adaptation.

Task B is often a subcase of Task C, as the intended user of “structure found in data” are humans (scientists) who pour over the representation of data to gain some intuition about its properties, and these “properties” can be often phrased as a classification task.

This post explains the relationship between Tasks A and C, and why they get mixed up in students’ mind. We hope there is also some food for thought here for experts, namely, our discussion about the fragility of the usual “perplexity” definition of unsupervised learning. It explains why Task A doesn’t in practice lead to good enough solution for Task C. For example, it has been believed for many years that for deep learning, unsupervised pretraining should help supervised training, but this has been hard to show in practice.
acmtariat  org:bleg  nibble  machine-learning  acm  thinking  clarity  unsupervised  conceptual-vocab  concept  explanation  features  bayesian  off-convex  deep-learning  latent-variables  generative  intricacy  distribution  sampling 
june 2017 by nhaliday
Trust, Trolleys and Social Dilemmas: A Replication Study
Overall, the present studies clearly confirmed the main finding of Everett et al., that deontologists are more trusted than consequentialists in social dilemma games. Study 1 replicates Everett et al.’s effect in the context of trust games. Study 2 generalizes the effect to public goods games, thus demonstrating that it is not specific to the type of social dilemma game used in Everett et al. Finally, both studies build on these results by demonstrating that the increased trust in deontologists may sometimes, but not always, be warranted: deontologists displayed increased cooperation rates but only in the public goods game and not in trust games.

The Adaptive Utility of Deontology: Deontological Moral Decision-Making Fosters Perceptions of Trust and Likeability: https://sci-hub.tw/http://link.springer.com/article/10.1007/s40806-016-0080-6
Consistent with previous research, participants liked and trusted targets whose decisions were consistent with deontological motives more than targets whose decisions were more consistent with utilitarian motives; this effect was stronger for perceptions of trust. Additionally, women reported greater dislike for targets whose decisions were consistent with utilitarianism than men. Results suggest that deontological moral reasoning evolved, in part, to facilitate positive relations among conspecifics and aid group living and that women may be particularly sensitive to the implications of the various motives underlying moral decision-making.

Inference of Trustworthiness From Intuitive Moral Judgments: https://sci-hub.tw/10.1037/xge0000165

Exposure to moral relativism compromises moral behavior: https://sci-hub.tw/http://www.sciencedirect.com/science/article/pii/S0022103113001339

Is utilitarian sacrifice becoming more morally permissible?: http://cushmanlab.fas.harvard.edu/docs/Hannikainanetal_2017.pdf

Disgust and Deontology: http://journals.sagepub.com/doi/abs/10.1177/1948550617732609
Trait Sensitivity to Contamination Promotes a Preference for Order, Hierarchy, and Rule-Based Moral Judgment

We suggest that a synthesis of these two literatures points to one specific emotion (disgust) that reliably predicts one specific type of moral judgment (deontological). In all three studies, we found that trait disgust sensitivity predicted more extreme deontological judgment.

The Influence of (Dis)belief in Free Will on Immoral Behavior: https://www.frontiersin.org/articles/10.3389/fpsyg.2017.00020/full

Beyond Sacrificial Harm: A Two-Dimensional Model of Utilitarian Psychology.: http://psycnet.apa.org/record/2017-57422-001
Recent research has relied on trolley-type sacrificial moral dilemmas to study utilitarian versus nonutilitarian modes of moral decision-making. This research has generated important insights into people’s attitudes toward instrumental harm—that is, the sacrifice of an individual to save a greater number. But this approach also has serious limitations. Most notably, it ignores the positive, altruistic core of utilitarianism, which is characterized by impartial concern for the well-being of everyone, whether near or far. Here, we develop, refine, and validate a new scale—the Oxford Utilitarianism Scale—to dissociate individual differences in the ‘negative’ (permissive attitude toward instrumental harm) and ‘positive’ (impartial concern for the greater good) dimensions of utilitarian thinking as manifested in the general population. We show that these are two independent dimensions of proto-utilitarian tendencies in the lay population, each exhibiting a distinct psychological profile. Empathic concern, identification with the whole of humanity, and concern for future generations were positively associated with impartial beneficence but negatively associated with instrumental harm; and although instrumental harm was associated with subclinical psychopathy, impartial beneficence was associated with higher religiosity. Importantly, although these two dimensions were independent in the lay population, they were closely associated in a sample of moral philosophers. Acknowledging this dissociation between the instrumental harm and impartial beneficence components of utilitarian thinking in ordinary people can clarify existing debates about the nature of moral psychology and its relation to moral philosophy as well as generate fruitful avenues for further research. (PsycINFO Database Record (c) 2017 APA, all rights reserved)

A breakthrough in moral psychology: https://nintil.com/2017/12/28/a-breakthrough-in-moral-psychology/

Gender Differences in Responses to Moral Dilemmas: A Process Dissociation Analysis: https://www.ncbi.nlm.nih.gov/pubmed/25840987
The principle of deontology states that the morality of an action depends on its consistency with moral norms; the principle of utilitarianism implies that the morality of an action depends on its consequences. Previous research suggests that deontological judgments are shaped by affective processes, whereas utilitarian judgments are guided by cognitive processes. The current research used process dissociation (PD) to independently assess deontological and utilitarian inclinations in women and men. A meta-analytic re-analysis of 40 studies with 6,100 participants indicated that men showed a stronger preference for utilitarian over deontological judgments than women when the two principles implied conflicting decisions (d = 0.52). PD further revealed that women exhibited stronger deontological inclinations than men (d = 0.57), while men exhibited only slightly stronger utilitarian inclinations than women (d = 0.10). The findings suggest that gender differences in moral dilemma judgments are due to differences in affective responses to harm rather than cognitive evaluations of outcomes.
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march 2017 by nhaliday
The genetics of politics: discovery, challenges, and progress
Figure 1. Summary of relative genetic and environmental influences on political traits.

- heritability increases discontinuously on leaving home
- pretty big range of heritability for different particular traits (party identification is lowest w/ largest shared environment by far)
- overall ideology quite highly heritable
- social trust is surprisingly highly compared other measurements I've seen...
- ethnocentrism quite low (sample-dependent?)
- authoritarianism and traditionalism quite high
- voter turnout quite high

Genes, psychological traits and civic engagement: http://rstb.royalsocietypublishing.org/content/370/1683/20150015
We show an underlying genetic contribution to an index of civic engagement (0.41), as well as for the individual acts of engagement of volunteering for community or public service activities (0.33), regularly contributing to charitable causes (0.28) and voting in elections (0.27). There are closer genetic relationships between donating and the other two activities; volunteering and voting are not genetically correlated. Further, we show that most of the correlation between civic engagement and both positive emotionality and verbal IQ can be attributed to genes that affect both traits.

Are Political Orientations Genetically Transmitted?: http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1006&context=poliscifacpub
TABLE 1. Genetic and Environmental Influences on Political Attitudes: The 28 Individual Wilson–Patterson Items

The origins of party identification and its relationship to political orientations: http://sci-hub.tw/http://www.sciencedirect.com/science/article/pii/S0191886915002470

All models showed a good overall fit (see Table 3). The data indicate that party identification is substantially heritable, with about 50% of the variation in PID attributable to additive genetic effects. Moreover, the results indicate that the non-genetic influences on party identification stem primarily from unique environmental factors rather than shared ones such as growing up in the same family. This too is not consistent with the Michigan model.

Table 3 also indicates that genetic influences explained about 50% of the variance in liberalism–conservatism. This estimate is similar to previous behavior genetic findings on political attitudes (e.g., Alford et al., 2005; Bouchard, 2004; Hatemi et al., 2014; Kandler, Bleidorn, & Riemann, 2012). The remaining variance was again due primarily to nonshared environmental influences. The latter finding indicates that the Michigan hypothesis that partisan social influences affect political orientations may have some merit, although the substantial level of heritability for this variable suggests that genetic effects also play an important role.

...

As Table 4 reveals, the best fitting model indicates that 100% of the genetic variance in PID is held in common with liberalism–conservatism ([aC2]/[aC2 + aPID2] = 1.00). Similarly, 73% of the environmental variation in PID is shared with liberalism–conservatism ([eC2]/[eC2 + ePID2] = .73). All told, only 13% of the total variance in PID cannot be explained by variation in liberalism–conservatism (1 [aC2 + eC2] = .13), as illustrated in Fig. 3. Since only a small proportion of the variance in PID cannot be explained by liberalism– conservatism, the findings are consistent with the hypothesis that genetic and environmental factors influence liberalism–conservatism, which in turn affects party identification. However, as discussed below, other causal scenarios cannot be ruled out.

Table 4 and Fig. 3 also show that 55% of the total variance in liberalism–conservatism cannot be accounted for by variance in PID

Fig. 3. Venn diagram mapping the common and specific variance in party
identification and liberalism–conservatism.

intuition for how you can figure out overlap of variance: look at how corr(PID, liberal-conservative) differs between MZ and DZ twin pairs, etc., fit structural equational model

p_k,i,j = r_A a_k,i,j,p + r_C c_k,i,p + r_E e_k,i,j,p (k=MZ or DZ, i=1..n_k, j=1,2, p=PID or LC value)

c_k,i,j,p = r_{C,p} c'_k,i,p + r_{C,common} c'_k,i,common (ditto)
e_k,i,j,p = r_{E,p} e'_k,i,j,p + r_{E,common} e'_k,i,j,common (ditto)

MZ twins:
a_MZ,i,j,p = r_{A,p} a'_MZ,i,p + r_{A,common} a'_MZ,i,common (i=1..n_k, j=1,2 p=PID or LC value)

DZ twins:
a_DZ,i,j,p = r_{A,p} (1/2 a'_DZ,i,p + 1/2 a'_DZ,i,j,p) + r_{A,common} (1/2 a'_DZ,i,common + 1/2 a'_DZ,i,j,common) (i=1..n_k, j=1,2 p=PID or LC value)

Gaussian distribution for the underlying a', c' and e' variables, maximum likelihood, etc.

see page 9 here: https://pinboard.in/u:nhaliday/b:70f8b5b559a9

basically:
1. calculate population means μ from data (so just numbers)
2. calculate covariance matrix Σ in terms of latent parameters r_A, r_C, etc. (so variable correlations)
3. assume observed values are Gaussian with those parameters μ, Σ
4. maximum likelihood to figure out the parameters r_A, r_C, etc.

A Genetic Basis of Economic Egalitarianism: http://sci-hub.tw/10.1007/s11211-017-0297-y
Our results show that the large portion of the variance in a four-item economic egalitarianism scale can be attributed to genetic factor. At the same time, shared environment, as a socializing factor, has no significant effect. The effect of environment seems to be fully reserved for unique personal experience. Our findings further problematize a long-standing view that social justice attitudes are dominantly determined by socialization.

published in the journal "Social Justice Research" by some Hungarians, lol

various political science findings, w/ a few behavioral genetic, focus on Trump, right-wing populism/authoritarianism, and polarization: http://www.nationalaffairs.com/blog/detail/findings-a-daily-roundup/a-bridge-too-far
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february 2017 by nhaliday
What is the difference between inference and learning? - Quora
- basically boils down to latent variables vs. (hyper-)parameters
- so computing p(x_h|x_v,θ) vs. computing p(θ|X_v)
- from a completely Bayesian perspective, no real difference
- described in more detail in [Kevin Murphy, 10.4]
q-n-a  qra  jargon  machine-learning  stats  acm  bayesian  graphical-models  latent-variables  confusion  comparison  nibble 
january 2017 by nhaliday
The genetic basis of social mobility | EVOLVING ECONOMICS
In 2007’s A Farewell to Alms: A Brief Economic History of the World, Gregory Clark argued that the higher fertility of the rich in pre-industrial England sowed the seeds for the Industrial Revolution. As children resemble their parents, the increased number of prudent, productive people made possible the modern economic era.

Part of the controversy underlying Clark’s argument – made stark by Clark in articles and speeches following A Farewell to Alm’s publication – was that he considered there may be a genetic basis to the transmitted traits. The higher fertility of the rich and changing character of the population was natural selection at work.

Clark’s new book The Son Also Rises: Surnames and the History of Social Mobility, also makes a new and unique argument. And like A Farewell to Alms, there is a genetic underlay.

Clark’s primary argument is that across a range of societies and eras – from pre-Industrial to modern England, from pre- to post-revolution China, and across the centuries in the United States, Sweden and India – social mobility is low. The correlation in social status between one generation and the next is around 0.7 to 0.8, meaning we can find the echoes of high status 10 or more generations later. Status does “regress to the mean”, but it does so slowly.
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november 2016 by nhaliday
The Son Also Rises | West Hunter
It turns out that you can predict a kid’s social status better if you take into account the grandparents as well as the parents – and the nieces/nephews, cousins, etc. Which means that you’re estimating the breeding value for moxie – which means that Clark needs to read Falconer right now. I’d guess that taking into account grandparents that the kids never even met, ones that died before their birth, will improve prediction. Let the sociologists chew on that.

...

If culture was the driver, a group could just adopt a different culture (it happens) and decide to be the new upper class by doing all that shit Amy Chua pushes, or possibly by playing cricket. I don’t believe that this ever actually occurs. Although with genetic engineering on the horizon, it may be possible. Of course that would be cheating.

It is hard to change these patterns very much. Universal public education, fluoridation, democracy, haven’t made much difference. I do think that shooting enough people would. Or a massive application of droit de seigneur, or its opposite.

...

If moxie is genetic, most economists must be wrong about human capital formation. Having fewer kids and spending more money on their education has only a modest effect: this must be the case, given slow long-run social mobility. It seems that social status is transmitted within families largely independently of the resources available to parents. Which is why Ashkenazi Jews could show up at Ellis Island flat broke, with no English, and have so many kids in the Ivy League by the 1920s that they imposed quotas. I’ve never understood why economists ever believed in this.

Moxie is not the same thing as IQ, although IQ must be a component. It is also worth remembering that this trait helps you acquire status – it is probably not quite the same thing as being saintly, honest, or incredibly competent at doing your damn job.

https://westhunt.wordpress.com/2014/03/24/simple-mobility-models/
https://westhunt.wordpress.com/2014/03/29/simple-mobility-models-ii/
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november 2016 by nhaliday

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