Review of Statistical Methodologies for Detecting Drug–Drug Interactions Using Spontaneous Reporting Systems

analysis of ruin probability for insurance companies using markov chain

در این پایان نامه نشان داده ایم که چگونه می توان مدل ریسک بیمه ای اسپیرر اندرسون را به کمک زنجیره های مارکوف تعریف کرد. سپس به کمک روش های آنالیز ماتریسی احتمال برشکستگی ، میزان مازاد در هنگام برشکستگی و میزان کسری بودجه در زمان وقوع برشکستگی را محاسبه کرده ایم. هدف ما در این پایان نامه بسیار محاسباتی و کاربردی تر از روش های است که در گذشته برای محاسبه این احتمال ارائه شده است. در ابتدا ما نشا...

Detecting Statistical Interactions from Neural Network Weights

We develop a method of detecting statistical interactions in data by directly interpreting the trained weights of a feedforward multilayer neural network. By structuring the neural network to statistical properties of data and applying sparsity regularization, we are able to leverage the weights to detect interactions with similar performance to the state-of-the-art without searching an exponen...

Detecting Statistical Interactions from Neural Network Weights

We develop a method of detecting statistical interactions in data by directly interpreting the trained weights of a feedforward multilayer neural network. With regularization applied to the weights, our method can achieve similar interaction detection performance compared to the state-of-the-art without searching an exponential solution space of possible interactions. We obtain our computationa...

Spontaneous Reporting Systems: Achieving Less Spontaneity and More Reporting

Detecting Reporting Anomalies in Streaming Sensing Systems

Sensor networks must be monitored to identify and correct problems as they occur. We present a comparison of two approaches to monitoring deployed sensors. The first relies on configuration parameters to define expected reporting behavior. The second automatically identifies normal reporting patterns based on a combination of configuration parameters and an analysis of reporting times. Using th...