Peter D Grünwald
Biographic Data
| ID | 5997252 |
|---|---|
| NAME | Peter D Grünwald |
| GIVEN NAMES | Peter D |
| FAMILY NAME | Grünwald |
| SIGNATURE | GRÜNWALD P D |
| AFFILIATIONS | Leiden University |
| ORCID | 0000-0001-9832-9936 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 6 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2012 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 1 |
Why optional stopping can be a problem for Bayesians
Recently, optional stopping has been a subject of debate in the Bayesian psychology community. Rouder ( Psychonomic Bulletin & Review 21 (2), 301–308, 2014) argues that optional stopping is no problem for Bayesians, and even recommends the use of optional stopping in practice, as do (Wagenmakers, Wetzels, Borsboom, van der Maas & Kievit, Perspectives on Psychological Science 7 , 627–633, 2012). This article addresses the question of whether optio…
The no-free-lunch theorems of supervised learning
The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others? Drawing parallels to the philosophy of induction, we point out that the no-free-lunch results presuppose a conception of learning algorithms as purely data-driven. On this conception, every algorithm must have…
Good Listeners, Wise Crowds, and Parasitic Experts
This article comments on the article of Thorn and Schurz in this volume and focuses on, what we call, the problem of parasitic experts. We discuss that both meta- induction and crowd wisdom can be understood as pertaining to absolute reliability rather than comparative optimality, and we suggest that the involvement of reliability will provide a handle on this problem
The no-free-lunch theorems of supervised learning
The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others? Drawing parallels to the philosophy of induction, we point out that the no-free-lunch results presuppose a conception of learning algorithms as purely data-driven. On this conception, every algorithm must have…
Good Listeners, Wise Crowds, and Parasitic Experts
This article comments on the article of Thorn and Schurz in this volume and focuses on, what we call, the problem of parasitic experts. We discuss that both meta- induction and crowd wisdom can be understood as pertaining to absolute reliability rather than comparative optimality, and we suggest that the involvement of reliability will provide a handle on this problem
Why optional stopping can be a problem for Bayesians
Recently, optional stopping has been a subject of debate in the Bayesian psychology community. Rouder ( Psychonomic Bulletin & Review 21 (2), 301–308, 2014) argues that optional stopping is no problem for Bayesians, and even recommends the use of optional stopping in practice, as do (Wagenmakers, Wetzels, Borsboom, van der Maas & Kievit, Perspectives on Psychological Science 7 , 627–633, 2012). This article addresses the question of whether optio…
The no-free-lunch theorems of supervised learning
The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others? Drawing parallels to the philosophy of induction, we point out that the no-free-lunch results presuppose a conception of learning algorithms as purely data-driven. On this conception, every algorithm must have…
Computer Science (3 works) · Artificial Intelligence (2 works) · Mathematics (2 works) · Active learning (machine learning) (1 work) · Advanced Bandit Algorithms Research (1 work) · Algorithm (1 work) · Algorithmic learning theory (1 work) · Artificial Intelligence (1 work) · Bayes factor (1 work) · Bayes' theorem (1 work)