andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2162 knowledge-graph by maker-knowledge-mining
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Introduction: Johannes Castner writes: Suppose there are k scientists, each with her own model (Bayesian Net) over m random variables. Then, because the space of Bayesian Nets over these m variables, with the square-root of the Jensen-Shannon Divergence as a distance metric is a closed and bounded space, there exists one unique Bayes Net that is a mixture of the k model joint-distributions which is at equal distance to each of the k models and may be called a “consensus graph.” This consensus graph is in turn a Bayes Net, which can be updated with evidence. The first question is: What are the conditions for which, given a new bit of evidence, the updated consensus graph is exactly the same graph as the consensus graph of the updated k Bayes Nets? In other words, if we arrive at a synthetic model from k models and then update this synthetic model, under what conditions is this the same thing as if we had first updated all k models and then build a synthesis. The second question is: If these ar
sentIndex sentText sentNum sentScore
1 Johannes Castner writes: Suppose there are k scientists, each with her own model (Bayesian Net) over m random variables. [sent-1, score-0.178]
2 ” This consensus graph is in turn a Bayes Net, which can be updated with evidence. [sent-3, score-1.058]
3 The first question is: What are the conditions for which, given a new bit of evidence, the updated consensus graph is exactly the same graph as the consensus graph of the updated k Bayes Nets? [sent-4, score-2.601]
4 In other words, if we arrive at a synthetic model from k models and then update this synthetic model, under what conditions is this the same thing as if we had first updated all k models and then build a synthesis. [sent-5, score-1.898]
5 The second question is: If these are not the same, then which of the two would be better and under what conditions, from the perspective of collective learning? [sent-6, score-0.288]
6 It all seems related to various topics of interest to me (see, for example, this presentation from 2003) but I don’t know anything about what he is talking about. [sent-8, score-0.405]
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Introduction: Johannes Castner writes: Suppose there are k scientists, each with her own model (Bayesian Net) over m random variables. Then, because the space of Bayesian Nets over these m variables, with the square-root of the Jensen-Shannon Divergence as a distance metric is a closed and bounded space, there exists one unique Bayes Net that is a mixture of the k model joint-distributions which is at equal distance to each of the k models and may be called a “consensus graph.” This consensus graph is in turn a Bayes Net, which can be updated with evidence. The first question is: What are the conditions for which, given a new bit of evidence, the updated consensus graph is exactly the same graph as the consensus graph of the updated k Bayes Nets? In other words, if we arrive at a synthetic model from k models and then update this synthetic model, under what conditions is this the same thing as if we had first updated all k models and then build a synthesis. The second question is: If these ar
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Introduction: In response to this article by Cosma Shalizi and myself on the philosophy of Bayesian statistics, David Hogg writes: I [Hogg] agree–even in physics and astronomy–that the models are not “True” in the God-like sense of being absolute reality (that is, I am not a realist); and I have argued (a philosophically very naive paper, but hey, I was new to all this) that for pretty fundamental reasons we could never arrive at the True (with a capital “T”) model of the Universe. The goal of inference is to find the “best” model, where “best” might have something to do with prediction, or explanation, or message length, or (horror!) our utility. Needless to say, most of my physics friends *are* realists, even in the face of “effective theories” as Newtonian mechanics is an effective theory of GR and GR is an effective theory of “quantum gravity” (this plays to your point, because if you think any theory is possibly an effective theory, how could you ever find Truth?). I also liked the i
Introduction: I continue to struggle to convey my thoughts on statistical graphics so I’ll try another approach, this time giving my own story. For newcomers to this discussion: the background is that Antony Unwin and I wrote an article on the different goals embodied in information visualization and statistical graphics, but I have difficulty communicating on this point with the infovis people. Maybe if I tell my own story, and then they tell their stories, this will point a way forward to a more constructive discussion. So here goes. I majored in physics in college and I worked in a couple of research labs during the summer. Physicists graph everything. I did most of my plotting on graph paper–this continued through my second year of grad school–and became expert at putting points at 1/5, 2/5, 3/5, and 4/5 between the x and y grid lines. In grad school in statistics, I continued my physics habits and graphed everything I could. I did notice, though, that the faculty and the other
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