Bayesian Nonparametric Models for Modelling Ecological Data and Stochastic Processes for Modelling Species Interactions

In this thesis, we present four manuscripts, described in the second to fifth chapter. Chapter 2 presents a Bayesian nonparametric model for capture-recapture (CR) data collected at different sites and for several years. To estimate arrival and departure patterns at the different sites and years, we build an extension of the Dirichlet process, the Hierarchical Dependent Dirichlet process, which allows us to perform density estimation jointly across different sites and in the presence of covariates. In this case, we use a year-specific covariate, and model the correlation structure of the covariate across years using a multivariate Gaussian process. In Chapter 3, we present a model for estimating entry and exit patterns, as well as the population size, using count data (CD), by employing a Polya Tree (PT) prior. In Chapter 4 we present several extensions of chapter 3. More specifically, we extend the model to CR and to ring-recovery data and develop a joint model for CR and CD. In addition, we consider the case when multiple data-sets are modelled at the same time, by defining a hierarchical extension of the PT, which we define as Hierarchical Logistic PT. Finally, we extend the model to the case of long time series, by borrowing ideas from the Optional PT. Chapter 5 presents a spatial model to estimate interactions between multiple species using CR data. The model uses a vector of interaction point process (IPP), which allows us to estimate interactions between and within species. The use of an IPP leads to an intractable ratio of normalising constants (RNC), and hence we use the Monte Carlo Metropolis Hastings algorithm to approximate the RNC with an importance sampling estimate. The supplementary material for each paper is presented in the appendix

Structured matrix recovery from matrix-vector products

Can one recover a matrix efficiently from only matrix-vector products? If so, how many are needed? This paper describes algorithms to recover matrices with known structures, such as tridiagonal, Toeplitz, Toeplitz-like, and hierarchical low-rank, from matrix-vector products. In particular, we derive a randomized algorithm for recovering an $N \times N$ unknown hierarchical low-rank matrix from only $\mathcal{O}((k+p)\log(N))$ matrix-vector products with high probability, where $k$ is the rank of the off-diagonal blocks, and $p$ is a small oversampling parameter. We do this by carefully constructing randomized input vectors for our matrix-vector products that exploit the hierarchical structure of the matrix. While existing algorithms for hierarchical matrix recovery use a recursive "peeling" procedure based on elimination, our approach uses a recursive projection procedure

Sub_Merge

This work takes as its starting point recorded sounds and measurements of water from North America, the Arctic, and Antarctica. Using field recordings, data measurements, and multiple temporalities as a factor for composition, we decode the recordings and measurements to make audible the various forms of ecological memory and story held within water. These include water quality data, sea-ice thickness, glacial weather data, and recordings made underwater and on the land.   Sub_Merge is a space and time for active listening consistent with a meditative practice, a mediation on the collective experiences possible within water. The embedded performance creates a boundary for shared listening, while the 6-channel installation is amorphous, unfolding over the 12-hour installation - a timescale allowing for engaged listener participation. Sub_Merge is a speculative soundscape for listeners to emerge with stories and new understandings of water in all its myriad forms. It creates a new collective listening experience built from an historical record of water’s Listening Pasts, and new knowledge for Listening Futures, from the memories held within water.&nbsp

Quality assessment metrics for edge detection and edge-aware filtering: A tutorial review

The quality assessment of edges in an image is an important topic as it helps to benchmark the performance of edge detectors, and edge-aware filters that are used in a wide range of image processing tasks. The most popular image quality metrics such as Mean squared error (MSE), Peak signal-to-noise ratio (PSNR) and Structural similarity (SSIM) metrics for assessing and justifying the quality of edges. However, they do not address the structural and functional accuracy of edges in images with a wide range of natural variabilities. In this review, we provide an overview of all the most relevant performance metrics that can be used to benchmark the quality performance of edges in images. We identify four major groups of metrics and also provide a critical insight into the evaluation protocol and governing equations

On Minimizing the Risk of Bias in Randomized Controlled Trials in Economics

Estimation of empirical relationships is prone to bias. Economists have carefully studied sources of bias in structural and quasi-experimental approaches, but the randomized control trial (RCT) has only begun to receive such scrutiny. In this paper, we argue that several lessons from medicine, derived from analysis of thousands of RCTs establishing a clear link between certain practices and biased estimates, can be used to reduce the risk of bias in economics RCTs. We identify the subset of these lessons applicable to economics and use them to assess risk of bias in estimates from economics RCTs published between 2001 and 2011. In comparison to medical studies, we find most economics studies do not report important details on study design necessary to assess risk of bias. Many report practices that suggest risk of bias, though this does not necessarily mean bias resulted. We conclude with suggestions on how to remedy these issues

Excerpt from El Hombre, La Hambra, Y El Hambre by Diana Chaviano

Translation of original Spanish text by Alfred López and Alex Fuentes