Imagine we want to know the ratio of men to women in a particular population. We could count all men and women one by one, but it would take too long, so instead we take a random sample and count the men and women in the sample, and from that we infer the quantity that we want to know. This is statistical inference.
In Bayesian inference, the population ratio is seen as a quantity that can take different values each with a associated probability (i.e. a random variable), and the result of Bayesian inference is an estimate of the probability distribution of the population parameter, in this case the population ratio. Now, in reality the population ratio is a concrete number, say 9-to-10, meaning that there are 9 men for every 10 women in the population. But Bayesians don't care. They'll tell you that the population ratio is a random variable which can take many values, and that the probability that it is equal to 9-to-10 is whatever number between 0 and 100%.
This is nonsense because the population ratio is NOT a random variable. People don't come in and out of existence randomly, right? In a way, they're saying there are infinitely many possible universes, each with a different population ratio, and then they come up with an estimate of the probability that the universe in which the ratio is 9-to-10 has whatever probability of occurring. This is absolutely BIZARRE. (I hope you agree). And it's wrong because it's impossible to know how likely one universe is compared to all other possible universes, since we live in our universe and this is all we can hope to observe.
> Bayesian inference assumes the observed data are fixed and aims to quantify the evidentiary support for all possible levels of treatment effectiveness based on the data at hand.
The problem with this approach is that we can only observe ONE level of treatment effectiveness, i.e., the level of treatment effectiveness that the treatment actually possesses. All other possible levels of effectiveness are entirely hypothetical. There's no data about all these other possible levels of effectiveness because they don't occur in reality. So the data cannot possibly tell you anything about how likely is the observed outcome, because the observed outcome is the only outcome that you observe. I
This criticism was made over 100 years ago, and Bayesians still don't have an answer. They just keep going as if nothing happened, but the reality is their methodology is utterly and fatally flawed.
> In Bayesian statistics, on the other hand, the parameter is not a point but a distribution.
To be more precise, in Bayesian statistics a parameter is random variable. But what does that mean? A parameter is a characteristic of a population (as opposed to a characteristic of a sample, which is called a statistic). A quantity, such as the average cars per household right now. That's a parameter. To think of a parameter as a random variable is like regarding reality as just one realisation of an infinite number of alternate realities that could have been. The problem is we only observe our reality. All the data samples that we can ever study come from this reality. As a result, it's impossible to infer anything about the probability distribution of the parameter. The whole Bayesian approach to statistical inference is nonsensical.
Retained earnings are not taxed per se. A company pays taxes on profits. Whether the profits are distributed to shareholders or retained makes no difference whatsoever as far as taxes are concerned.
If I deposit dollars in a savings account I will get paid interest, but that is different from the dollar itself being an interest-bearing asset. I think the same thing applies to stablecoins. Does USDC pay interest to the holder or do I have to make a USDC deposit at Coinbase in order to get paid interest? Also, banks already offer a ton of products that generate yield. I don't see why a product that seems relatively similar to many products that banks already offer would destroy their business... unless such a product is much better than what banks offer, but that doesn't seem to be the case.
The whole point of working out is to stress the organism in order to induce a physiological adaptation. Inflammation is NOT the point, but rather an unfortunate side effect.
This is not how you compare two industries. Comparing Visa to Ethereum, in terms of the value of the service that they provide, is not a problem at all, in fact it's very easy. For that we would use gross value added, which is the value of output minus the value of goods and services employed to produce such output. For example, in the case of Visa and Ethereum, the output is processing of financial transactions, and the market value of that output are the fees that customers pay to have their transaction processed. From that, we have to subtract the costs of all the resources employed by Visa and Ethereum to provide the service, respectively, to get the value added. These are all known quantities, which tell us exactly the market value of the services provided by these companies and/or industries, so that we can compare one with another. Market cap is absolutely irrelevant to that end.
That's a lot of money being used in defi (whatever that means) but do you have a single example of financing through defi? Financing means providing borrowers with resources (such as money) that they don't already have. I think you'll have a hard time finding examples, because it turns out financing requires the ability to seize assets from a defaulting borrower, an ability which defi lacks.
Decentralisation doesn't help with inflation, it only makes everything more expensive due to the extravagant cost of the consensus mechanism. Banks are already decentralised (should be obvious since there are many banks, not just one) and competition among banks is what prevents them from charging high fees.
I wrote another comment here clarifying my point, if you're interested: https://news.ycombinator.com/item?id=47566033