Bayesian updating in causal probabilistic networks by local computations pdf. Donald Bren School of Information and Computer Sciences.



Bayesian updating in causal probabilistic networks by local computations pdf

Bayesian updating in causal probabilistic networks by local computations pdf

Because When you are facing in the wrong direction, progress means walking backwards. History suggests that our world view undergoes disruptive change not so much when science adds new concepts to our cognitive toolkit, but when it takes away old ones.

The sets of intuitions that have been with us since birth define our scientific prejudices, and not only are poorly-suited to the realms of the very large and very small, but also fail to describe everyday phenomena. If we are to identify where the next transformation of our world view will come from, we need to take a fresh look at our deep intuitions. In the two minutes that it takes you to read this essay, I am going to try and rewire your basic thinking about causality.

Causality is usually understood as meaning that there is a single, preceding cause for an event. For example in classical physics, a ball may be flying through the air, because of having been hit by a tennis racket. My 16 year-old car always revs much too fast, because the temperature sensor wrongly indicates that the engine temperature is cold, as if the car was in start-up mode.

We are so familiar with causality as an underlying feature of reality that we hard-wire it into the laws of physics. It might seem that this would be unnecessary, but it turns out that the laws of physics do not distinguish between time going backwards and time going forwards. And so we make a choice about which sort of physical law we would like to have. However, complex systems, such as financial markets or the Earth's biosphere, do not seem to obey causality.

For every event that occurs, there are a multitude of possible causes, and the extent to which each contributes to the event is not clear, not even after the fact! One might say that there is a web of causation. For example, on a typical day, the stock market might go up or down by some fraction of a percentage point. The Wall Street Journal might blithely report that the stock market move was due to "traders taking profits" or perhaps "bargain-hunting by investors".

The following day, the move might be in the opposite direction, and a different, perhaps contradictory, cause will be invoked. However, for each transaction, there is both a buyer and a seller, and their world views must be opposite for the transaction to occur. Markets work only because there is a plurality of views.

To assign single or dominant cause to most market moves is to ignore the multitude of market outlooks and to fail to recognize the nature and dynamics of the temporary imbalances between the numbers of traders who hold these differing views. Similar misconceptions abound elsewhere in public debate and the sciences.

For example, are there single causes for diseases? In some cases, such as Huntingdon's disease, the cause can be traced to a unique factor, in this case extra repetitions of a particular nucleotide sequence at a particular location in an individual's DNA, coding for the amino acid glutamine. However, even in this case, the age of onset and the severity of the condition are also known to be controlled by environmental factors and interactions with other genes.

The web of causation has been for many decades a well-worked metaphor in epidemiology, but there is still little quantitative understanding of how the web functions or forms. As Krieger poignantly asked in a celebrated essay, "Has anyone seen the spider? Fueling the debate is a fundamental notion of causality, that there is a beginning to life, and that such a beginning must have had a single cause. On the other hand, if there is instead a web of causation driving the origin and evolution of life, a skeptic might ask: It turns out that there is no spider.

Webs of causation can form spontaneously through the concatenation of associations between the agents or active elements in the system. For example, consider the Internet. Remarkably, once the dust began to settle, it became apparent that the statistical properties of the resulting Internet were quite special: However, you look at the Internet, locally or globally, on short time scales or long, it looks exactly the same.

Although the discovery of this fractal structure around was an unwelcome surprise, because standard traffic control algorithms as used by routers were designed assuming that all properties of the network dynamics would be random, the fractality is also broadly characteristic of biological networks. Without a master blueprint, the evolution of an Internet is subject to the same underlying statistical laws that govern biological evolution, and structure emerges spontaneously without the need for a controlling entity.

Moreover, the resultant network can come to life in strange and unpredictable ways, obeying new laws whose origin cannot be traced to any one part of the network.

The network behaves as a collective, not just the sum of parts, and to talk about causality is meaningless because the behavior is distributed in space and in time. This disruption occurred as part of a tumultuous event on that day now known as the Flash Crash, which affected numerous market indices and individual stocks, even causing some stocks to be priced at unbelievable levels e. Accenture was at one point priced at 1 cent. With tick-by-tick data available for every trade, we can watch the crash unfold in slow motion, a film of a financial calamity.

But the cause of the crash itself remains a mystery. The conditions that precipitate the crash were already embedded in the market's web of causation, a self-organized rapidly evolving structure created by the interplay of high frequency trading algorithms. The Flash Crash was the birth cry of a network coming to life, eerily reminiscent of Arthur C. I guess I don't really know.

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Jeffrey Cardille - Bayesian Updating of Land-cover Classifications



Bayesian updating in causal probabilistic networks by local computations pdf

Because When you are facing in the wrong direction, progress means walking backwards. History suggests that our world view undergoes disruptive change not so much when science adds new concepts to our cognitive toolkit, but when it takes away old ones. The sets of intuitions that have been with us since birth define our scientific prejudices, and not only are poorly-suited to the realms of the very large and very small, but also fail to describe everyday phenomena. If we are to identify where the next transformation of our world view will come from, we need to take a fresh look at our deep intuitions.

In the two minutes that it takes you to read this essay, I am going to try and rewire your basic thinking about causality. Causality is usually understood as meaning that there is a single, preceding cause for an event. For example in classical physics, a ball may be flying through the air, because of having been hit by a tennis racket. My 16 year-old car always revs much too fast, because the temperature sensor wrongly indicates that the engine temperature is cold, as if the car was in start-up mode.

We are so familiar with causality as an underlying feature of reality that we hard-wire it into the laws of physics. It might seem that this would be unnecessary, but it turns out that the laws of physics do not distinguish between time going backwards and time going forwards.

And so we make a choice about which sort of physical law we would like to have. However, complex systems, such as financial markets or the Earth's biosphere, do not seem to obey causality. For every event that occurs, there are a multitude of possible causes, and the extent to which each contributes to the event is not clear, not even after the fact!

One might say that there is a web of causation. For example, on a typical day, the stock market might go up or down by some fraction of a percentage point. The Wall Street Journal might blithely report that the stock market move was due to "traders taking profits" or perhaps "bargain-hunting by investors".

The following day, the move might be in the opposite direction, and a different, perhaps contradictory, cause will be invoked.

However, for each transaction, there is both a buyer and a seller, and their world views must be opposite for the transaction to occur. Markets work only because there is a plurality of views. To assign single or dominant cause to most market moves is to ignore the multitude of market outlooks and to fail to recognize the nature and dynamics of the temporary imbalances between the numbers of traders who hold these differing views.

Similar misconceptions abound elsewhere in public debate and the sciences. For example, are there single causes for diseases? In some cases, such as Huntingdon's disease, the cause can be traced to a unique factor, in this case extra repetitions of a particular nucleotide sequence at a particular location in an individual's DNA, coding for the amino acid glutamine. However, even in this case, the age of onset and the severity of the condition are also known to be controlled by environmental factors and interactions with other genes.

The web of causation has been for many decades a well-worked metaphor in epidemiology, but there is still little quantitative understanding of how the web functions or forms.

As Krieger poignantly asked in a celebrated essay, "Has anyone seen the spider? Fueling the debate is a fundamental notion of causality, that there is a beginning to life, and that such a beginning must have had a single cause. On the other hand, if there is instead a web of causation driving the origin and evolution of life, a skeptic might ask: It turns out that there is no spider. Webs of causation can form spontaneously through the concatenation of associations between the agents or active elements in the system.

For example, consider the Internet. Remarkably, once the dust began to settle, it became apparent that the statistical properties of the resulting Internet were quite special: However, you look at the Internet, locally or globally, on short time scales or long, it looks exactly the same.

Although the discovery of this fractal structure around was an unwelcome surprise, because standard traffic control algorithms as used by routers were designed assuming that all properties of the network dynamics would be random, the fractality is also broadly characteristic of biological networks. Without a master blueprint, the evolution of an Internet is subject to the same underlying statistical laws that govern biological evolution, and structure emerges spontaneously without the need for a controlling entity.

Moreover, the resultant network can come to life in strange and unpredictable ways, obeying new laws whose origin cannot be traced to any one part of the network. The network behaves as a collective, not just the sum of parts, and to talk about causality is meaningless because the behavior is distributed in space and in time.

This disruption occurred as part of a tumultuous event on that day now known as the Flash Crash, which affected numerous market indices and individual stocks, even causing some stocks to be priced at unbelievable levels e. Accenture was at one point priced at 1 cent. With tick-by-tick data available for every trade, we can watch the crash unfold in slow motion, a film of a financial calamity.

But the cause of the crash itself remains a mystery. The conditions that precipitate the crash were already embedded in the market's web of causation, a self-organized rapidly evolving structure created by the interplay of high frequency trading algorithms. The Flash Crash was the birth cry of a network coming to life, eerily reminiscent of Arthur C.

I guess I don't really know.

Bayesian updating in causal probabilistic networks by local computations pdf

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  1. Data-efficient solutions under small noise exist, such as PILCO which learns the cartpole swing-up task in 30s.

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