The Unity of Fitness - Philsci
... Case 1: In a species with sexual reproduction, probabilities of numbers of grandoffspring will depend on offspring’s mates as well parents’ number of offspring. Probabilities over numbers of offspring thus don’t imply long-term probabilities; probabilities over number of grandoffspring seem needed f ...
... Case 1: In a species with sexual reproduction, probabilities of numbers of grandoffspring will depend on offspring’s mates as well parents’ number of offspring. Probabilities over numbers of offspring thus don’t imply long-term probabilities; probabilities over number of grandoffspring seem needed f ...
The spacey random walk: a stochastic process for higher-order data
... Without the stochastic constraints on the vector entries, x is called a z eigenvector [Qi, 2005] or an l2 eigenvector [Lim, 2005] of P . Li and Ng [2014] and Gleich et al. [2015] analyze when a solution vector x for Equation 1.4 exists and provide algorithms for computing the vector. These algorithm ...
... Without the stochastic constraints on the vector entries, x is called a z eigenvector [Qi, 2005] or an l2 eigenvector [Lim, 2005] of P . Li and Ng [2014] and Gleich et al. [2015] analyze when a solution vector x for Equation 1.4 exists and provide algorithms for computing the vector. These algorithm ...
On The Quantitative Definition of Risk
... seems “soft” and changeable, subjective- not measureable, at least not in the usual way. The cornerstone of our approach is the idea that given two meaningful statements (or propositions or events), it makes sense to say that one is more (less, ...
... seems “soft” and changeable, subjective- not measureable, at least not in the usual way. The cornerstone of our approach is the idea that given two meaningful statements (or propositions or events), it makes sense to say that one is more (less, ...
Combining Micro and Macro Unemployment Duration Data
... As a result, empirical inference is non-standard in the sense that the stochastic elements in the micro and macro observations are from dierent distributions. Nevertheless, the model can be estimated by maximum likelihood methods, maximizing the product of the two likelihood functions associated wi ...
... As a result, empirical inference is non-standard in the sense that the stochastic elements in the micro and macro observations are from dierent distributions. Nevertheless, the model can be estimated by maximum likelihood methods, maximizing the product of the two likelihood functions associated wi ...
A Concentration Inequalities B Benchmark C
... From Steps 1 and 2, this implies a lower bound on E[ t=1 rt (at )]. The proof is now completed by using Azuma-Hoeffding to bound the actual total reward with high probability. ...
... From Steps 1 and 2, this implies a lower bound on E[ t=1 rt (at )]. The proof is now completed by using Azuma-Hoeffding to bound the actual total reward with high probability. ...
Mixing Methods: A Bayesian Approach
... The approach presented here is also connected to strategies for ecological inference that combine case-level information with population-level information to better estimate population-level causal effects (Glynn et al., 2008). ...
... The approach presented here is also connected to strategies for ecological inference that combine case-level information with population-level information to better estimate population-level causal effects (Glynn et al., 2008). ...
Estimation of Bacterial Densities by Means of the" Most Probable
... theory of inverse probability may have been responsible. During the past 25 years the problem of making estimates from data has received much attention from statisticians. Today, most statisticiaiis would, I believe, reject an appeal to intuition or to the theory of inverse probability as a reliable ...
... theory of inverse probability may have been responsible. During the past 25 years the problem of making estimates from data has received much attention from statisticians. Today, most statisticiaiis would, I believe, reject an appeal to intuition or to the theory of inverse probability as a reliable ...
here
... algorithms, Gen, Enc, Dec, and the generated key k were kept private; the idea was of course that the less information we give to the adversary, the harder it is to break the scheme. A design principle formulated by Kerchoff in 1884— known as Kerchoff’s principle—instead stipulates that the only thi ...
... algorithms, Gen, Enc, Dec, and the generated key k were kept private; the idea was of course that the less information we give to the adversary, the harder it is to break the scheme. A design principle formulated by Kerchoff in 1884— known as Kerchoff’s principle—instead stipulates that the only thi ...
A Context-aware Time Model for Web Search
... the search engine results are. The ability to predict time-to-lastclick allows us to suggest queries that require less time (effort) to satisfy user’s information needs. The predicted times can be used as features for query suggestion and for other applications such as prediction of search task diff ...
... the search engine results are. The ability to predict time-to-lastclick allows us to suggest queries that require less time (effort) to satisfy user’s information needs. The predicted times can be used as features for query suggestion and for other applications such as prediction of search task diff ...
Zero-Information Protocols and Unambiguity in Arthur–Merlin
... Interestingly, Neq and Gt demonstrate that privacy can come at a huge cost, since UAM(Neq) ≤ BPP(Neq) ≤ O(log n) and similarly for Gt, and thus there is an exponential separation between ZAM and UAM. It remains open to show that there exists a function (even a random one!) that requires superlinear ...
... Interestingly, Neq and Gt demonstrate that privacy can come at a huge cost, since UAM(Neq) ≤ BPP(Neq) ≤ O(log n) and similarly for Gt, and thus there is an exponential separation between ZAM and UAM. It remains open to show that there exists a function (even a random one!) that requires superlinear ...
More Efficient PAC-learning of DNF with Membership Queries Under
... Jackson [15] gave the first polynomial-time PAC learning algorithm for DNF with membership queries under the uniform distribution. However, the algorithm’s time and sample complexity make it impractical for all but relatively small problems. The algorithm is also not particularly efficient in its us ...
... Jackson [15] gave the first polynomial-time PAC learning algorithm for DNF with membership queries under the uniform distribution. However, the algorithm’s time and sample complexity make it impractical for all but relatively small problems. The algorithm is also not particularly efficient in its us ...
Optimal Illusion of Control and Related Perception Biases - cerge-ei
... To this aim, we provide a definition of the loss associated with each misperception of q instead of p that builds on the standard economic welfare approach. The misperceived probability of the high state distorts choice and leads to a welfare loss in some of the encountered decision problems. We defin ...
... To this aim, we provide a definition of the loss associated with each misperception of q instead of p that builds on the standard economic welfare approach. The misperceived probability of the high state distorts choice and leads to a welfare loss in some of the encountered decision problems. We defin ...
Probability box
A probability box (or p-box) is a characterization of an uncertain number consisting of both aleatoric and epistemic uncertainties that is often used in risk analysis or quantitative uncertainty modeling where numerical calculations must be performed. Probability bounds analysis is used to make arithmetic and logical calculations with p-boxes.An example p-box is shown in the figure at right for an uncertain number x consisting of a left (upper) bound and a right (lower) bound on the probability distribution for x. The bounds are coincident for values of x below 0 and above 24. The bounds may have almost any shapes, including step functions, so long as they are monotonically increasing and do not cross each other. A p-box is used to express simultaneously incertitude (epistemic uncertainty), which is represented by the breadth between the left and right edges of the p-box, and variability (aleatory uncertainty), which is represented by the overall slant of the p-box.