Machine-Learning-Based Classification for Pipeline Corrosion with Monte Carlo Probabilistic Analysis

Multi-tree Monte Carlo methods for fast, scalable machine learning

MOCA ARM: Analog Reliability Measurement based on Monte Carlo Analysis

Due to the expected increase of defects in circuits based on deep submicron technologies, reliability has become an important design criterion. Although different approaches have been developed to estimate reliability in digital circuits and some measuring concepts have been separately presented to reveal the quality of analog circuit reliability in the literature, there is a gap to estimate re...

Body Mass Index Classification based on Facial Features using Machine Learning Algorithms for utilizing in Telemedicine

Background and Objectives: Due to the impact of controlling BMI on life, BMI classification based on facial features can be used for developing Telemedicine systems and eliminating the limitations of measuring tools, especially for paralyzed people. So that physicians can help people online during the Covid-19 pandemic. Method: In this study, new features and some previous work features were e...

Probabilistic Path Hamiltonian Monte Carlo

6. Appendix 6.1. Properties of the phylogenetic posterior distribution Assumption 2.3 for the phylogenetic posterior distribution. Recall that L(⌧, q) denotes the likelihood function of the tree T = (⌧, q), we have U(⌧, q) = logL(⌧, q) log ⇡0(⌧, q) Since log ⇡0(⌧, q) is assumed to satisfy the Assumption 2.3, we just need to prove that the phylogenetic likelihood function is smooth while each or...

Probabilistic Path Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) is an efficient and effective means of sampling posterior distributions on Euclidean space, which has been extended to manifolds with boundary. However, some applications require an extension to more general spaces. For example, phylogenetic (evolutionary) trees are defined in terms of both a discrete graph and associated continuous parameters; although one can rep...