Everything is Predictable - Revision history

Robert.adlington: /* Conclusion: Bayesian Life */

2025-11-18T18:50:42Z

Conclusion: Bayesian Life

Robert.adlington: /* 5. The Bayesian Brain */

2025-11-18T18:46:22Z

5. The Bayesian Brain

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 * Crucially, what you experience is not the data from your sense, but your predictions - predictions constantly updated by information from the sense, yes, but the world you live in is the prediction, not the data.
 * Anil Seth: What we experience is best described as a Bayesian inference about the causes of sensory data."
 === Conclusion: Bayesian Life ===

Robert.adlington: /* 5. The Bayesian Brain */

2025-11-18T17:22:10Z

5. The Bayesian Brain

← Older revision		Revision as of 17:22, 18 November 2025
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	=== 5. The Bayesian Brain ===		=== 5. The Bayesian Brain ===

			* Perception and consciousness itself is - in quite a direct sense - Bayesian. You're faced with ambiguous sensory information and are going from observations to what caused those observations - that's inverse reasoning.
			* Various scientists argue that the central thing the brain does is build predictions of the world, which it then integrates with information coming in via the senses. Perception is a two-way street: information travels up from our sense, but it also travels down from our internal model of the universe. Our perception is the commingling of that bottom-up stream with the top-down one. The two constrain each other - if the top-down priors are strong, then it requires precise, strong evidence from the senses to overturn them.
			* Neuroscientist Chris Frith says that our perception of reality is a controlled hallucination.
			* When everything is going as expected we are not even aware of it. But when something unexpected happens, the low-level processor bumps the problem one place higher up the chain. If the slightly-higher-level processor can explain it, then it will do so, and well send new signals down the chain. If it can't then the new information keeps going up the chain.
			* Your brain is taking information from various modalities - vision, hearing, touch, small, and taste, but also your internal sense of where your body is and how it's arranged and whether you're hungry or thirsty or horny or whatever - and combining them.
			* Crucially, what you experience is not the data from your sense, but your predictions - predictions constantly updated by information from the sense, yes, but the world you live in is the prediction, not the data.
			* Anil Seth: What we experience is best described as a Bayesian inference about the causes of sensory data."

	=== Conclusion: Bayesian Life ===		=== Conclusion: Bayesian Life ===

Robert.adlington: /* 4. Bayes in the World */

2025-11-18T17:12:29Z

4. Bayes in the World

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 === 4. Bayes in the World ===
 === 5. The Bayesian Brain ===
 === Conclusion: Bayesian Life ===

Robert.adlington: /* 3. Bayesian Decision Theory */

2025-11-17T08:23:42Z

3. Bayesian Decision Theory

← Older revision		Revision as of 08:23, 17 November 2025
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	=== 3. Bayesian Decision Theory ===		=== 3. Bayesian Decision Theory ===

			* George Boole described the operations of propositional logic as "the laws of thought".
			* In our reasoning we depend very much on prior information to help us in evaluating the degree of plausibility in a new problem. This reasoning process goes on unconsciously, almost instantaneously, and we conceal how complicated it really is by calling it common sense.
			* Cromwell's rule: You should never be certain.
			* Probability and utility together make expected value.
			* If humans were perfect reasoning machines with full access to our own underlying preferences, we could work out the maths, using a combination of Bayes and utility theorem. That is, roughly speaking, what modern AI does, in a much more explicit way.
			* Hyperpriors are to do with uncertaintly about what world you're in. You use hyperparameters to determine your hyperpriors, and then this will restrict the parameters from which you will choose your priors.

	=== 4. Bayes in the World ===		=== 4. Bayes in the World ===

Robert.adlington: /* 2. Bayes in Science */

2025-11-16T15:31:39Z

2. Bayes in Science

← Older revision		Revision as of 15:31, 16 November 2025
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	=== 2. Bayes in Science ===		=== 2. Bayes in Science ===

			* In 2011, science underwent the replication crisis.
			** Hypothesizing after results are known or HARKing or p-hacking.
			** Scientific literature prioritizes novel findings, incentivizes (even if unconsciously) p-hacking.
			* Popper's philosophy of science said that you never prove a scientific hypothesis - you only disprove it or fail to do so. The Bayesian idea that you can build up evidence for or against is very much opposed to it.
			* Hume: "All our experimental conclusions proceed upon the supposition that the future will be conformable to the past."
			* Popper: "We choose the theory which best holds its own in competition with other theories; the one which, by natural selection, proves itself the fittest to survive. This will be the one which not only has hitherto stood up to the severest tests, but the one which is also testable in the most rigorous way." He called such a theory "corroborated".
			* Instinctively, at least, scientists think like Bayesians.
			* In frequentist experiments, the p-value can rise and fall suddenly as the sample size gets bigger. for Bayesians, though, there is always prior data and so each new data point coming in moves your opinion much less, and form part of the new prior for your next bit of information.
			* Bayesian techniques don't just reject or accept the null hypothesis - they don't just say yes or no to a hypothesis, but give degrees of belief to a range of possible realities. In reality, there is no such thing as a null hypothesis, or rather the null hypothesis is always, ultimately false.
			* Bayesian's make an estimate of the size of an effect and give a probability distribution:
			** If your prior is very strong - your curve is really tall and narrow - and the new data is fairly weak, giving a likelihood curve that is low and wide, then the resulting curve will look more like the prior.
			** If your prior is weak but you've got really good data, so your likelihood curve is tall and pointy, the new data will wash our the prior, and the posterior will be more like the likelihood.
			** If the posterior curve is tall and pointy compared to the prior, then you have something noteworthy and worth following up.

	=== 3. Bayesian Decision Theory ===		=== 3. Bayesian Decision Theory ===

Robert.adlington: /* 1. From the Book of Common Prayer to the Full Monty Carlo */

2025-11-16T15:13:22Z

1. From the Book of Common Prayer to the Full Monty Carlo

← Older revision		Revision as of 15:13, 16 November 2025
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	*** A p-value is the likelihood of seeing results at least as extreme as those you've seen, given the null hypothesis. P-values of		*** A p-value is the likelihood of seeing results at least as extreme as those you've seen, given the null hypothesis. P-values of
	*** The null hypothesis is the hypothesis that whatever effect you're looking for isn't real. Eg hair color has no effect on soup eating.		*** The null hypothesis is the hypothesis that whatever effect you're looking for isn't real. Eg hair color has no effect on soup eating.
			**All our lives, we are in a sense betting. Whenever we go to the station, we are betting that a train will really run, and if we had not a sufficient degree of belief in this, we should decline the bet and stay at home.

	=== 2. Bayes in Science ===		=== 2. Bayes in Science ===

Robert.adlington: /* Introduction: A Theory of Not Quite Everything */

2025-11-16T10:53:46Z

Introduction: A Theory of Not Quite Everything

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 === Introduction: A Theory of Not Quite Everything ===
 === 1. From the Book of Common Prayer to the Full Monty Carlo ===
 === 2. Bayes in Science ===

Robert.adlington: Created page with "=== Introduction: A Theory of Not Quite Everything === === 1. From the Book of Common Prayer to the Full Monty Carlo === === 2. Bayes in Science === === 3. Bayesian Decision Theory === === 4. Bayes in the World === === 5. The Bayesian Brain === === Conclusion: Bayesian Life ==="

2025-11-15T17:57:16Z

Created page with "=== Introduction: A Theory of Not Quite Everything === === 1. From the Book of Common Prayer to the Full Monty Carlo === === 2. Bayes in Science === === 3. Bayesian Decision Theory === === 4. Bayes in the World === === 5. The Bayesian Brain === === Conclusion: Bayesian Life ==="

New page

=== Introduction: A Theory of Not Quite Everything ===

=== 1. From the Book of Common Prayer to the Full Monty Carlo ===

=== 2. Bayes in Science ===

=== 3. Bayesian Decision Theory ===

=== 4. Bayes in the World ===

=== 5. The Bayesian Brain ===

=== Conclusion: Bayesian Life ===

← Older revision		Revision as of 18:50, 18 November 2025
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	***There is evidence that psilocybin reduces depression. Psychedelics make you really interested in things. They can flatten your priors, make them less precise and upweight the data coming from your senses. For a depressed person, it will flatten their belief landscape - weaken their inappropriately strong priors.		***There is evidence that psilocybin reduces depression. Psychedelics make you really interested in things. They can flatten your priors, make them less precise and upweight the data coming from your senses. For a depressed person, it will flatten their belief landscape - weaken their inappropriately strong priors.
	***Another theory is that depression may be due to underconfidence in neural predictions.		***Another theory is that depression may be due to underconfidence in neural predictions.
	***For Karl Friston, minimizing prediction error isn't just sense-making, but is our fundamental motivation. Hunger sexual desire, boredom - all our wants and needs - can be described in terms of a struggle to reduce the difference between top-down prediction and bottom-up sense data, between your prior and your posterior distributions:		*For Karl Friston, minimizing prediction error isn't just sense-making, but is our fundamental motivation. Hunger sexual desire, boredom - all our wants and needs - can be described in terms of a struggle to reduce the difference between top-down prediction and bottom-up sense data, between your prior and your posterior distributions:
	****Being hungry is the same as confidently predicting that you are currently eating a sandwich but that prediction being wrong.		**Being hungry is the same as confidently predicting that you are currently eating a sandwich but that prediction being wrong.
	****The driver of all life, from bacteria on up, is to reduce the difference between what they predict and what they experience. You cannot change your model of what your body temperature is or what your glucose levels are, outside very narrow windows. So the only way of minimizing prediction error is by changing the world, or your position in it, so that your predictions are true.		**The driver of all life, from bacteria on up, is to reduce the difference between what they predict and what they experience. You cannot change your model of what your body temperature is or what your glucose levels are, outside very narrow windows. So the only way of minimizing prediction error is by changing the world, or your position in it, so that your predictions are true.
	****For Friston, that's what's going on in all self-organizing systems. We need to maintain homeostasis. But more sophisticated animals can plan ahead, manage their surrounding to minimize expected prediction error or expected surprise.		**For Friston, that's what's going on in all self-organizing systems. We need to maintain homeostasis. But more sophisticated animals can plan ahead, manage their surrounding to minimize expected prediction error or expected surprise.
	****Very fundamental priors are wired into us by evolution - blood-sugar levels, body temperature, oxygen levels, and bodily integrity and, to a lesser extent, perhaps, social and sexual desires. As young children the hardwired priors are all we have, but they start progressively to learn preferred states of being that you can work towards, which are constrained by these innate priors underneath them, which keep you alive.		**Very fundamental priors are wired into us by evolution - blood-sugar levels, body temperature, oxygen levels, and bodily integrity and, to a lesser extent, perhaps, social and sexual desires. As young children the hardwired priors are all we have, but they start progressively to learn preferred states of being that you can work towards, which are constrained by these innate priors underneath them, which keep you alive.
	****Babies do motor babbling, trying out random nerve signals and seeing what happens. They're learning you've got a body and certain things you can control and certain things you can't. At first they have so little information that they act at random, but their movements become increasingly sophisticated as they update their priors, learning to grasp food, feeding themselves, choosing which food to eat, to buy...		**Babies do motor babbling, trying out random nerve signals and seeing what happens. They're learning you've got a body and certain things you can control and certain things you can't. At first they have so little information that they act at random, but their movements become increasingly sophisticated as they update their priors, learning to grasp food, feeding themselves, choosing which food to eat, to buy...
	****The difference between a virus and you and me is how far into the future you can look. We have hierarchically deeper generative models, and what accompanies that is the ability to roll out further into the future.		**The difference between a virus and you and me is how far into the future you can look. We have hierarchically deeper generative models, and what accompanies that is the ability to roll out further into the future.
	****Some people find it weird to suggest that hunger is the same as wrongly predicting that you've eaten. But it's an elegant theory.		**Some people find it weird to suggest that hunger is the same as wrongly predicting that you've eaten. But it's an elegant theory.

	=== Conclusion: Bayesian Life ===		=== Conclusion: Bayesian Life ===

			* You can see the genome of an organism as a prediction about the world. Evolution is slow, blind, and inefficient - it might take hundreds of generations to solve a problem a human designer could solve in an hour - but it's an approximation of a Bayesian process: it works to minimize prediction error.
			* When we're young we have very little data about the world, so our priors are weak and new information can shift them easily. We can learn quickly, because we don't have a very precise model of the world that makes good predictions. As we get older, we gain more information, we get a richer, more precise model of the world, and new information must logically shift our priors less. So older people are wise but inflexible.