2001 Submissions

[3] viXra:2001.0052 [pdf] submitted on 2020-01-04 16:39:29

Marginal Likelihood Computation for Model Selection and Hypothesis Testing: an Extensive Review

Authors: F. Llorente, L. Martino, D. Delgado, J. Lopez-Santiago
Comments: 58 Pages.

This is an up-to-date introduction to, and overview of, marginal likelihood computation for model selection and hypothesis testing. Computing normalizing constants of probability models (or ratio of constants) is a fundamental issue in many applications in statistics, applied mathematics, signal processing and machine learning. This article provides a comprehensive study of the state-of-the-art of the topic. We highlight limitations, benets, connections and differences among the dierent techniques. Problems and possible solutions with the use of improper priors are also described. Some of the most relevant methodologies are compared through theoretical comparisons and numerical experiments.
Category: Statistics

[2] viXra:2001.0037 [pdf] submitted on 2020-01-03 14:40:30

Anomaly Detection for Cybersecurity: Time Series Forecasting and Deep Learning

Authors: Giordano Colò
Comments: 32 Pages.

Finding anomalies when dealing with a great amount of data creates issues related to the heterogeneity of different values and to the difficulty of modelling trend data during time. In this paper we combine the classical methods of time series analysis with deep learning techniques, with the aim to improve the forecast when facing time series with long-term dependencies. Starting with forecasting methods and comparing the expected values with the observed ones, we will find anomalies in time series. We apply this model to a bank cybersecurity case to find anomalous behavior related to branches applications usage.
Category: Statistics

[1] viXra:2001.0003 [pdf] submitted on 2020-01-01 06:38:26

Probability Models and Ultralogics

Authors: Robert A. Herrmann
Comments: 10 Pages.

In this paper, we show how nonstandard consequence operators, ultralogics, can generate the general informational content displayed by probability models. In particular, a model that states a specific probability that an event will occur and those models that use a specific distribution to predict that an event will occur. These results have many diverse applications and even apply to the collapse of the wave function.
Category: Statistics