Olasılık konusu öğrencilere neden zor gelmektedir?



Olasılık öğretiminde farklı bir yaklaşım: İnanç ve frekans bakış açıları
Kazak, Sibel (2022-09-01)
Quantitative measures of observability for stochastic systems
Subaşı, Yüksel; Demirekler, Mübeccel; Department of Electrical and Electronics Engineering (2012)
The observability measure based on the mutual information between the last state and the measurement sequence originally proposed by Mohler and Hwang (1988) is analyzed in detail and improved further for linear time invariant discrete-time Gaussian stochastic systems by extending the definition to the observability measure of a state sequence. By using the new observability measure it is shown that the unobservable states of the deterministic system have no effect on this measure and any observable part wit...
Probabilistic matrix factorization based collaborative filtering with implicit trust derived from review ratings information
Ercan, Eda; Taşkaya Temizel, Tuğba; Department of Information Systems (2010)
Recommender systems aim to suggest relevant items that are likely to be of interest to the users using a variety of information resources such as user profiles, trust information and users past predictions. However, typical recommender systems suffer from poor scalability, generating incomprehensible and not useful recommendations and data sparsity problem. In this work, we have proposed a probabilistic matrix factorization based local trust boosted recommendation system which handles data sparsity, scalabil...
The Interaction of probability learning and prefrontal cortex
Gözenman, Filiz; Gökçay, Didem; Çakır, Murat Perit; Department of Cognitive Sciences (2012)
Probability learning is the ability to establish a relationship between stimulus and outcomes based on occurrence probabilities using repetitive feedbacks. Participants learn the task according to the cue-outcome relationship, and try to gain in depth understanding of this relationship throughout the experiment. While learning is at the highest level, people rely on their working memory. In this study 20 participants were presented a probability learning task, and their prefrontal cortex activity was measur...
Gene function inference from expression using probabilistic topic models
Tercan, Bahar; Acar, Aybar Can; Department of Medical Informatics (2016)
The main aim of this study is to develop a probabilistic biclustering approach which can help to elaborate on the question "Can we determine the biological context of a sample (tissue/condition etc.) using expression data and associate the contexts with annotation databases like Gene Ontology, KEGG and HUGE to discover annotations (like cell division, metabolic process, illness etc.) for these contexts?". We applied a nonparametric probabilistic topic model, Hierarchical Dirichlet Process (HDP), which was o...
Citation Formats
S. Kazak, Olasılık konusu öğrencilere neden zor gelmektedir? 2009.