Raw 2009 -THE ROLE OF LATEST FIXATIONS ON ONGOING VISUAL SEARCH A MODEL TO E...Giacomo Veneri
The aim of the study is to understand the selection process, that modulates the exploration mechanism, during the execution of a high cognitively demanding task. The main purpose is to identify the mechanism competition mechanism between top-down and bottom-up. We developed an adaptive system trying to emulate this mechanism.
Developing visual material can help to recall memory and also be a quick way to show lots of information. Visualization helps us remember (like when we try to picture where we’ve parked our car, and what's in our cupboards when writing a shopping list). We can create diagrams and visual aids depicting module materials and put them up around the house so that we are constantly reminded of our learning
Probability and random processes project based learning template.pdfVedant Srivastava
To understand the concept of Monte –Carlo Method and its various applications and it rely on repeated and random sampling to obtain numerical result.
Developing the computational algorithms to solve the problem related to random sampling.
Objective also contains simulation of specific problem in Matlab Software.
Dr Chris Drovandi (QUT) - Bayesian Indirect Inference Using a Parametric Auxi...QUT_SEF
Dr Chris Drovandi, statistical science lecturer at QUT, discusses a general framework for likelihood-free Bayesian inference problems called Bayesian Indirect Likelihood (BIL). There is a focus on some specific instances of the BIL framework that use in some way a parametric auxiliary model, which is an alternative model that possesses a tractable likelihood function. These methods are referred to as parametric Bayesian Indirect Inference (pBII) methods.
One class of pBII methods uses the score or parameter of the auxiliary model to form summary statistics for ABC. A different class (called parametric BIL, pBIL) uses the likelihood (either at the full-data or summary statistic level) of the auxiliary model as a replacement to the likelihood of the true model.
The theoretical aspects of pBIL are explored and contrasted against the ABC approach that uses summary statistics formed from the auxiliary model. The theoretical results and the performance of the methods will be demonstrated on examples of varying complexity, including estimating the parameters of a stochastic process for macroparasite population evolution.
This is joint work with Prof. Tony Pettitt, Dr Anthony Lee and Leah South.
Introduction about Monte Carlo Methods, lecture given at Technical University of Kaiserslautern 2014.
There are many situations where Monte Carlo Methods are useful to solve data science problems
Raw 2009 -THE ROLE OF LATEST FIXATIONS ON ONGOING VISUAL SEARCH A MODEL TO E...Giacomo Veneri
The aim of the study is to understand the selection process, that modulates the exploration mechanism, during the execution of a high cognitively demanding task. The main purpose is to identify the mechanism competition mechanism between top-down and bottom-up. We developed an adaptive system trying to emulate this mechanism.
Developing visual material can help to recall memory and also be a quick way to show lots of information. Visualization helps us remember (like when we try to picture where we’ve parked our car, and what's in our cupboards when writing a shopping list). We can create diagrams and visual aids depicting module materials and put them up around the house so that we are constantly reminded of our learning
Probability and random processes project based learning template.pdfVedant Srivastava
To understand the concept of Monte –Carlo Method and its various applications and it rely on repeated and random sampling to obtain numerical result.
Developing the computational algorithms to solve the problem related to random sampling.
Objective also contains simulation of specific problem in Matlab Software.
Dr Chris Drovandi (QUT) - Bayesian Indirect Inference Using a Parametric Auxi...QUT_SEF
Dr Chris Drovandi, statistical science lecturer at QUT, discusses a general framework for likelihood-free Bayesian inference problems called Bayesian Indirect Likelihood (BIL). There is a focus on some specific instances of the BIL framework that use in some way a parametric auxiliary model, which is an alternative model that possesses a tractable likelihood function. These methods are referred to as parametric Bayesian Indirect Inference (pBII) methods.
One class of pBII methods uses the score or parameter of the auxiliary model to form summary statistics for ABC. A different class (called parametric BIL, pBIL) uses the likelihood (either at the full-data or summary statistic level) of the auxiliary model as a replacement to the likelihood of the true model.
The theoretical aspects of pBIL are explored and contrasted against the ABC approach that uses summary statistics formed from the auxiliary model. The theoretical results and the performance of the methods will be demonstrated on examples of varying complexity, including estimating the parameters of a stochastic process for macroparasite population evolution.
This is joint work with Prof. Tony Pettitt, Dr Anthony Lee and Leah South.
Introduction about Monte Carlo Methods, lecture given at Technical University of Kaiserslautern 2014.
There are many situations where Monte Carlo Methods are useful to solve data science problems
Presentation by Tommy Lofstedt, Associated Professor at Umeå University (Sweden), at the FogGuru Workshop on linking with other disciplines in October 2019.
9. S, G, and the Version Space most specific hypothesis, S most general hypothesis, G h H , between S and G is consistent and make up the version space (Mitchell, 1997)
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13. Multiple Classes , C i i=1,...,K Train hypotheses h i ( x ), i =1,..., K :
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