- ELSEVIER SCIENCE BV
- Biblioteca Digitais de Teses e Dissertações da USP
- Universidade Estadual Paulista
- BioMed Central
- Faculdade de Ciências e Tecnologia
- Harvard University
- University of Rochester. Computer Science Department.
- Universidade Nacional da Austrália
- Springer; Berlin
- Universidade de Tubinga
- Universidade Rice
- Université de Montréal
- University of Cambridge; Department of Physics; Cavendish Laboratory; Robinson College
- Universidade Duke
- Rochester Instituto de Tecnologia
- Mais Publicadores...

## Improved testing inference in mixed linear models

## Inferência de redes de regulação gênica utilizando o paradigma de crescimento de sementes; Inference of gene regulatory networks using the seed growing paradigm

## Tuning of fuzzy inference systems through unconstrained optimization techniques

## Efficient parametric adjustment of fuzzy inference system using unconstrained optimization

## Enabling network inference methods to handle missing data and outliers

## Type inference for conversation types

## Stochastic Modeling and Bayesian Inference with Applications in Biophysics

## Probabilistic Inference and Non-Monotonic Inference

## Probabilistic Inference and Probabilistic Reasoning

## A system of interaction and structure II: the need for deep inference

## An improved algorithm of multicast topology inference from end-to-end measurements

## Do films make you think? - Inference processes in expository film comprehension; Regen Filme zum Denken an? - Inferenzprozesse beim expositorischen Filmverstehen

## Bayesian graphical models for biological network inference

## Identification, Weak Instruments and Statistical Inference in Econometrics

## Inférence topologique

## Approximate inference in graphical models

## Statistical Inference Utilizing Agent Based Models

Agent-based models (ABMs) are computational models used to simulate the behaviors,

actionsand interactions of agents within a system. The individual agents

each have their own set of assigned attributes and rules, which determine

their behavior within the ABM system. These rules can be

deterministic or probabilistic, allowing for a great deal of

flexibility. ABMs allow us to

observe how the behaviors of the individual agents affect the system

as a whole and if any emergent structure develops within the

system. Examining rule sets in conjunction with corresponding emergent

structure shows how small-scale changes can

affect large-scale outcomes within the system. Thus, we can better

understand and predict the development and evolution of systems of

interest.

ABMs have become ubiquitous---they used in business

(virtual auctions to select electronic ads for display), atomospheric

science (weather forecasting), and public health (to model epidemics).

But there is limited understanding of the statistical properties of

ABMs. Specifically, there are no formal procedures

for calculating confidence intervals on predictions, nor for

assessing goodness-of-fit...

## The evolution of transitive inference: Chimpanzees’ performance with social and nonsocial stimuli

## Bayesian Mixture Modeling Approaches for Intermediate Variables and Causal Inference

This thesis examines causal inference related topics involving intermediate variables, and uses Bayesian methodologies to advance analysis capabilities in these areas. First, joint modeling of outcome variables with intermediate variables is considered in the context of birthweight and censored gestational age analyses. The proposed methodology provides improved inference capabilities for birthweight and gestational age, avoids post-treatment selection bias problems associated with conditional on gestational age analyses, and appropriately assesses the uncertainty associated with censored gestational age. Second, principal stratification methodology for settings where causal inference analysis requires appropriate adjustment of intermediate variables is extended to observational settings with binary treatments and binary intermediate variables. This is done by uncovering the structural pathways of unmeasured confounding affecting principal stratification analysis and directly incorporating them into a model based sensitivity analysis methodology. Demonstration focuses on a study of the efficacy of influenza vaccination in elderly populations. Third, flexibility, interpretability, and capability of principal stratification analyses for continuous intermediate variables are improved by replacing the current fully parametric methodologies with semiparametric Bayesian alternatives. This presentation is one of the first uses of nonparametric techniques in causal inference analysis...