23 papers · ranked by Valyu relevance
Richard R. Stein, Debora S. Marks, Chris Sander, Shi-Jie Chen
Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical…
Curtis Grant, Aukosh Jagannath, Justin Ko
We develop a pseudo-likelihood theory for rank one matrix estimation problems in the high dimensional limit. We prove a variational principle for the limiting pseudo-maximum likelihood which also characterizes the performance of the corresponding pseudo-maximum likelihood estimator. We show that this variational…
Jason D. Lee, Trevor Hastie
We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning. In previous work, authors have considered structure learning of…
Jiafan Zhu, Luay Nakhleh
Phylogenetic networks represent reticulate evolutionary histories. Statistical methods for their inference under the multispecies coalescent have recently been developed. A particularly powerful approach uses data that consist of bi-allelic markers (e.g., single nucleotide polymorphism data) and allows for exact…
Soumya Chakraborty, Ayanendranath Basu, Abhik Ghosh
Robust estimation under multivariate normal (MVN) mixture model is always a computational challenge. A recently proposed maximum pseudo β-likelihood estimator aims to estimate the unknown parameters of a MVN mixture model in the spirit of minimum density power divergence (DPD) methodology but with a relatively simpler…
Nicolae Sapoval, Zejian Liu, Mehrdad Tamiji, Meng Li + 1 more
Likelihood-based inference under the multispecies coalescent provides accurate estimates of species trees. However, maximum likelihood and Bayesian inference are both computationally very demanding. Pseudo-likelihood has been previously proposed as a computationally efficient alternative to full phylogenetic likelihood…
Marek Omelka, Šárka Hudecová, Natalie Neumeyer
This paper deals with a situation when one is interested in the dependence structure of a multidimensional response variable in the presence of a multivariate covariate. It is assumed that the covariate affects only the marginal distributions through regression models while the dependence structure, which is described…
Arindam Chatterjee, Tathagata Bandyopadhyay, Sumanta Adhya
Logistic regression is an extensively used regression model for binary responses. In many applications, misclassification of binary responses is not uncommon. If the misclassification is ignored, it may severely bias the maximum likelihood estimators (MLE) of regression parameters towards zero. To obviate this…
Shogo Yamanaka, Masayuki Ohzeki, Aurélien Decelle
We expand the item response theory to study the case of "cheating students" for a set of exams, trying to detect them by applying a greedy algorithm of inference. This extended model is closely related to the Boltzmann machine learning. In this paper we aim to infer the correct biases and interactions of our model by…
Tyler Williamson, Misha Eliasziw, Gordon Hilton Fick
Background Relative risk is a summary metric that is commonly used in epidemiological investigations. Increasingly, epidemiologists are using log-binomial models to study the impact of a set of predictor variables on a single binary outcome, as they naturally offer relative risks. However, standard statistical software…
Antonio Calcagnì, Livio Finos, Gianmarco Altoé, Massimiliano Pastore
In this article, we provide initial findings regarding the problem of solving likelihood equations by means of a maximum entropy (ME) approach. Unlike standard procedures that require equating the score function of the maximum likelihood problem at zero, we propose an alternative strategy where the score is instead…
Ludger Starke, Dirk Ostwald
Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation…
Authors not listed
Knowledge of the reaction rate constants can be vital in understanding electrochemical reaction mechanisms and their rate-determining processes. Although first-principles methods, such as density functional theory (DFT), provide valuable insight into reaction free energies and rate constants, they commonly use…
Van N. T. La, Stanley Nicholson, Amna Haneef, Lulu Kang + 1 more
Some data are just underappreciated. Maybe they look different or come from a different background than most other data. Maybe they don't fit neatly into common notions of what data on a ``curve'' should look like. Whatever the case, they are pigeonholed into a restricted role that limits their contributions. But if…
Subhash R. Lele, C. George Glen, José Miguel Ponciano, Yoh Iwasa + 1 more
'Boris Ryabko'] Due to the high dimensional integration over latent variables, computing marginal likelihood and posterior distributions for the parameters of a general hierarchical model is a difficult task. The Markov Chain Monte Carlo (MCMC) algorithms are commonly used to approximate the posterior distributions.…
Daiver Vélez Ramos, Luis R. Pericchi Guerra, María Eglée Pérez Hernández, Carlos Alberto De Bragança Pereira + 3 more
'María Eglée Pérez Hernández' 'Carlos Alberto De Bragança Pereira' 'Adriano Polpo' 'Agatha Rodrigues' 'Débora Corrêa'] Minimum Bayes factors are commonly used to transform two-sided p-values to lower bounds on the posterior probability of the null hypothesis, in particular the bound $-e\cdotp\cdotlog(p)$. This bound is…
Martin Robinson, Alan Bond, Alexandr Simonov, Jie Zhang + 1 more
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that the technique of large amplitude ac voltammetry yielded significantly more accurate parameter values than the equivalent dc approach. In…
E. G. Cooch, D. I. MacKenzie, J. A. Royle
Data augmentation is now a standard device across capture–recapture and occupancy analysis: adding a fixed number M of all-zero encounter histories replaces a model of unknown dimension with one of fixed dimension. Although M is often treated as a computational tuning choice, it also specifies a finite superpopulation…
David Colquhoun
We wish to answer this question If you observe a “significant” P value after doing a single unbiased experiment, what is the probability that your result is a false positive?. The weak evidence provided by P values between 0.01 and 0.05 is explored by exact calculations of false positive rates. When you observe P =…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
S. R. White, T. Kypraios, S. P. Preston
Many modern statistical applications involve inference for complicated stochastic models for which the likelihood function is difficult or even impossible to calculate, and hence conventional likelihood-based inferential techniques cannot be used. In such settings, Bayesian inference can be performed using Approximate…