Search results for " Inference"
showing 10 items of 337 documents
Thompson Sampling Guided Stochastic Searching on the Line for Non-stationary Adversarial Learning
2015
This paper reports the first known solution to the N-Door puzzle when the environment is both non-stationary and deceptive (adversarial learning). The Multi-Armed-Bandit (MAB) problem is the iconic representation of the exploration versus exploitation dilemma. In brief, a gambler repeatedly selects and play, one out of N possible slot machines or arms and either receives a reward or a penalty. The objective of the gambler is then to locate the most rewarding arm to play, while in the process maximize his winnings. In this paper we investigate a challenging variant of the MAB problem, namely the non-stationary N-Door puzzle. Here, instead of directly observing the reward, the gambler is only…
Conditional measures and their applications to fuzzy sets
1991
Abstract Given a ⊥-decomposable measure with respect to a continuous t-conorm, as introduced by the author in an earlier paper (see Section 1), we can construct ⊥-conditional measures as implications. These fulfil a ‘generalized product law’ replacing the product in the classical law by any other strict t-norm ⊥ and turn out to be decomposable with respect to an operation ⊥ V depending on ⊥, ⊥ and the condition set V (Section 2). More general, conditional measures are introduced axiomatically and are shown to be ⊥-conditional measures with respect to some ⊥-decomposable measure (Section 3). ‘Bayesian-like’ models are given which are alternatives to that presented by the author in a recent p…
Recent Advances in Bayesian Inference in Cosmology and Astroparticle Physics Thanks to the MultiNest Algorithm
2012
We present a new algorithm, called MultiNest, which is a highly efficient alternative to traditional Markov Chain Monte Carlo (MCMC) sampling of posterior distributions. MultiNest is more efficient than MCMC, can deal with highly multi-modal likelihoods and returns the Bayesian evidence (or model likelihood, the prime quantity for Bayesian model comparison) together with posterior samples. It can thus be used as an all-around Bayesian inference engine. When appropriately tuned, it also provides an exploration of the profile likelihood that is competitive with what can be obtained with dedicated algorithms.
Inferences of dietary preferences of Miocene squirrels (Xerinae, Sciuridae) from the Iberian Peninsula and Namibia using microwear analyses and ename…
2020
In this work, we compared microwear features and enamel thickness from upper molars (M1 and M2) of extinct Xerinae squirrels from the Miocene of Namibia (Vulcanisciurus sp) and the Iberian Peninsula (Atlantoxerus nov. sp. and Heteroxerus rubricati). We also examined the microwear from young and adult specimens of one extant squirrel, Atlantoxerus getulus, to compare it with the extinct species. Both, the microwear features and enamel thickness showed that the Miocene African species presented a more abrasive diet than the Iberian ones.
Awareness and Partitional Informational Structures
1997
We begin with an example to motivate the introduction of the concept of unawareness in models of information. There are a subject and two possible states of the world, σ and τ. At σ a certain fact p happens — it is true — and the subject sees it or hears it or anyhow perceives it, so that he knows it is true (in Geanakoplos [5] the subject is Sherlock Holmes’ assistant and fact p is ‘the dog barks’). At state τ fact p does not occur (it is false), and the subject not only does not see it or hear it etc.; but what is more, he does not even think of the possibility that it might: fact p is not present to the subject’s mind. What is an appropriate formal model for this story?
The Bayesian Learning Automaton — Empirical Evaluation with Two-Armed Bernoulli Bandit Problems
2009
The two-armed Bernoulli bandit (TABB) problem is a classical optimization problem where an agent sequentially pulls one of two arms attached to a gambling machine, with each pull resulting either in a reward or a penalty. The reward probabilities of each arm are unknown, and thus one must balance between exploiting existing knowledge about the arms, and obtaining new information.
On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata
2013
Published version of an article in the journal: Applied Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/s10489-013-0424-x There are currently two fundamental paradigms that have been used to enhance the convergence speed of Learning Automata (LA). The first involves the concept of utilizing the estimates of the reward probabilities, while the second involves discretizing the probability space in which the LA operates. This paper demonstrates how both of these can be simultaneously utilized, and in particular, by using the family of Bayesian estimates that have been proven to have distinct advantages over their maximum likelihood counterparts. The success of LA-…
Rejection odds and rejection ratios: A proposal for statistical practice in testing hypotheses
2016
Much of science is (rightly or wrongly) driven by hypothesis testing. Even in situations where the hypothesis testing paradigm is correct, the common practice of basing inferences solely on p-values has been under intense criticism for over 50 years. We propose, as an alternative, the use of the odds of a correct rejection of the null hypothesis to incorrect rejection. Both pre-experimental versions (involving the power and Type I error) and post-experimental versions (depending on the actual data) are considered. Implementations are provided that range from depending only on the p-value to consideration of full Bayesian analysis. A surprise is that all implementations -- even the full Baye…
Bayesian Inference for the Exponential Power Function Parameters
2008
This paper addresses the problem of obtaining the marginal posterior distributions, via Gibbs Sampler, for the parameters of the well-known generalized error distribution called Exponential Power Function (E.P.F.). This density represents a family of unimodal symmetric distributions with shapes varying from leptokurtic to platikurtic.
What can chromosomes tell us about the origins of primates?
2010
What can chromosomes tell us about the origins of primates? Barbara Picone1, Luca Sineo1, Daniele Silvestro2,3, Massimiliano DelPero4 and Judith Masters5 1 Dipartimento di Biologia Animale “G. Reverberi”, Università degli Studi di Palermo, Via Archirafi 18, 90123 Palermo, Italy; 2 Senckenberg Research Institute, Frankfurt am Main, Germany ; 3 Biodiversity and Climate Research Centre (BiK-F), Frankfurt am Main, Germany;4 Dipartimento di Biologia Animale e dell’Uomo, Università degli Studi di Torino, Via Accademia Albertina 13, 10124 Torino, Italy; 5Department of Zoology and Entomology, University of Fort Hare, Private Bag X1314, Alice 5700, South Africa; Our study investigated the usefulness…