Search results for " Inference"

A Project Manager Suitability Parameter in Project Accomplishment

2009

One the most critical aspect in project management is how to assign the project managers (PMs) to projects, especially whenever the PMs can lead more than one project. The present paper proposes a parameter (PME) to evaluate the PM in accomplishing a specific project useful for a next phase of assignment. The PME takes into account the technical skills, the leadership behavior and the relationships with project’s stakeholders. These parameters are aggregated by a Fuzzy Inference System (FIS) that well emulates the decision process of the experts by means of a rule-based inference engine. Moreover, to better define the PME, a procedure, based on the discordance concept, is proposed to compar…

Local Monitor Implementation for Decentralized Intrusion Detection in Secure Multi–Agent Systems

2007

This paper focuses on the detection of misbehav- ing agents within a group of mobile robots. A novel approach to automatically synthesize a decentralized Intrusion Detection System (IDS) as well as an efficient implementation of local monitors are presented. In our scenario, agents perform possi- bly different independent tasks, but cooperate to guarantee the entire system’s safety. Indeed, agents plan their next actions by following a set of logic rules which is shared among them. Such rules are decentralized, i.e. they depend only on configurations of neighboring agents. However, some agents may not be acting according to this cooperation protocol, due to spontaneous failure or tampering.…

E-negotiator based on buyer's surfing pattern

2017

Everyone likes to get the best price, but best price cannot be provided to everyone. This paper proposes an algorithm to provide the best price to the most prospective buyer. E-Negotiation will be based on buyer's activity pattern and surfing behavior. Unlike existing e-negotiation models proposed for B2C, B2B and C2C Ecommerce applications, where the intervention of buyer is present, this paper proposes a system to e-negotiate by just observing buyer's surfing pattern, without depending on his input. Surfing patterns such as sites visited and products surfed will tell the buyer's intention on purchasing the product. The amount of discount for negotiation is generated using a Fuzzy Inferenc…

Bayesian inference analysis of the uncertainty linked to the evaluation of potential flood damage in urban areas.

2012

Flood damage in urbanized watersheds may be assessed by combining the flood depth–damage curves and the outputs of urban flood models. The complexity of the physical processes that must be simulated and the limited amount of data available for model calibration may lead to high uncertainty in the model results and consequently in damage estimation. Moreover depth–damage functions are usually affected by significant uncertainty related to the collected data and to the simplified structure of the regression law that is used. The present paper carries out the analysis of the uncertainty connected to the flood damage estimate obtained combining the use of hydraulic models and depth–damage curve…

Basic Statistical Techniques

2012

Simplifying Probabilistic Expressions in Causal Inference

2018

Obtaining a non-parametric expression for an interventional distribution is one of the most fundamental tasks in causal inference. Such an expression can be obtained for an identifiable causal effect by an algorithm or by manual application of do-calculus. Often we are left with a complicated expression which can lead to biased or inefficient estimates when missing data or measurement errors are involved. We present an automatic simplification algorithm that seeks to eliminate symbolically unnecessary variables from these expressions by taking advantage of the structure of the underlying graphical model. Our method is applicable to all causal effect formulas and is readily available in the …

A perspective on Gaussian processes for Earth observation

2019

Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and Gaussian processes (GPs) in particular has attained outstanding results in the estimation of bio-geo-physical variables from the acquired images at local and global scales in a time-resolved manner. GPs provide not only accurate estimates but also principled uncertainty estimates for the predictions, can easily accommodate multimodal data coming from different sensors and from multitemporal acquisitions, allow the introduction of physical knowledge, and a formal treatment of uncertainty quantification and error pr…

Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference

2018

This letter introduces warped Gaussian process (WGP) regression in remote sensing applications. WGP models output observations as a parametric nonlinear transformation of a GP. The parameters of such a prior model are then learned via standard maximum likelihood. We show the good performance of the proposed model for the estimation of oceanic chlorophyll content from multispectral data, vegetation parameters (chlorophyll, leaf area index, and fractional vegetation cover) from hyperspectral data, and in the detection of the causal direction in a collection of 28 bivariate geoscience and remote sensing causal problems. The model consistently performs better than the standard GP and the more a…

Deep Importance Sampling based on Regression for Model Inversion and Emulation

2021

Understanding systems by forward and inverse modeling is a recurrent topic of research in many domains of science and engineering. In this context, Monte Carlo methods have been widely used as powerful tools for numerical inference and optimization. They require the choice of a suitable proposal density that is crucial for their performance. For this reason, several adaptive importance sampling (AIS) schemes have been proposed in the literature. We here present an AIS framework called Regression-based Adaptive Deep Importance Sampling (RADIS). In RADIS, the key idea is the adaptive construction via regression of a non-parametric proposal density (i.e., an emulator), which mimics the posteri…

Probabilistic and team PFIN-type learning: General properties

2008

We consider the probability hierarchy for Popperian FINite learning and study the general properties of this hierarchy. We prove that the probability hierarchy is decidable, i.e. there exists an algorithm that receives p_1 and p_2 and answers whether PFIN-type learning with the probability of success p_1 is equivalent to PFIN-type learning with the probability of success p_2. To prove our result, we analyze the topological structure of the probability hierarchy. We prove that it is well-ordered in descending ordering and order-equivalent to ordinal epsilon_0. This shows that the structure of the hierarchy is very complicated. Using similar methods, we also prove that, for PFIN-type learning…