The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Deviance Information Criterion (DIC) are perhaps the most widely-used information criteria (IC) in model building and selection. A fourth, Minimum Description Length (MDL), is closely related to the BIC. In a nutshell, they provide guidance as which alternative model provides the most "bang for buck," i.e., the best fit after penalizing for model complexity. Penalizing for complexity is important since, given candidate models of similar predictive or explanatory power, the simplest model is most likely to be the best choice. In line with Occam's razor, complex models sometimes perform poorly on data not used in the model building. There are several others, including AIC3, SABIC, and CAIC, and no clear consensus among authorities as far as I am aware as to which is "best" overall. IC will not necessarily agree on which model should be chosen. Cross-validation, Predicted Residual Error Sum of Squares (PRESS) statistic, a kind of cross-validation, and Mallows’ Cp are also used instead of IC. Information criteria are covered in varying levels in detail in most statistics textbooks and are the subject of numerous academic papers. I know of no single go-to source on this topic.
The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Deviance Information Criterion (DIC) are perhaps the most widely-used information criteria (IC) in model building and selection. A fourth, Minimum Description Length (MDL), is closely related to the BIC. In a nutshell, they provide guidance as which alternative model provides the most "bang for buck," i.e., the best fit after penalizing for model complexity. Penalizing for complexity is important since, given candidate models of similar predictive or explanatory power, the simplest model is most likely to be the best choice. In line with Occam's razor, complex models sometimes perform poorly on data not used in the model building. There are several others, including AIC3, SABIC, and CAIC, and no clear consensus among authorities as far as I am aware as to which is "best" overall. IC will not necessarily agree on which model should be chosen. Cross-validation, Predicted Residual Error Sum of Squares (PRESS) statistic, a kind of cross-validation, and Mallows’ Cp are also used instead of IC. Information criteria are covered in varying levels in detail in most statistics textbooks and are the subject of numerous academic papers. I know of no single go-to source on this topic.
Bitcoin is a decentralized digital currency that you can buy, sell and exchange directly, without an intermediary like a bank. Bitcoin’s creator, Satoshi Nakamoto, originally described the need for “an electronic payment system based on cryptographic proof instead of trust.” Each and every Bitcoin transaction that’s ever been made exists on a public ledger accessible to everyone, making transactions hard to reverse and difficult to fake. That’s by design: Core to their decentralized nature, Bitcoins aren’t backed by the government or any issuing institution, and there’s nothing to guarantee their value besides the proof baked in the heart of the system. “The reason why it’s worth money is simply because we, as people, decided it has value—same as gold,” says Anton Mozgovoy, co-founder & CEO of digital financial service company Holyheld.
Dump Scam in Leaked Telegram Chat
A leaked Telegram discussion by 50 so-called crypto influencers has exposed the extraordinary steps they take in order to profit on the back off unsuspecting defi investors. According to a leaked screenshot of the chat, an elaborate plan to defraud defi investors using the worthless “$Few” tokens had been hatched. $Few tokens would be airdropped to some of the influencers who in turn promoted these to unsuspecting followers on Twitter.
