29 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…
Michel Broniatowski, Igal Sason
This paper states that most commonly used minimum divergence estimators are MLEs for suited generalized bootstrapped sampling schemes. Optimality in the sense of Bahadur for associated tests of fit under such sampling is considered.
Akatsuki Kimura, Antonio Celani, Hiromichi Nagao, Timothy Stasevich + 1 more
'Kazuyuki Nakamura'] Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental…
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…
Clancy, David, Lyu, Hanbaek + 2 more
We consider the branch-length estimation problem on a bifurcating tree: a character evolves along the edges of a binary tree according to a two-state symmetric Markov process, and we seek to recover the edge transition probabilities from repeated observations at the leaves. This problem arises in phylogenetics, and is…
Antonio Calcagnì, Livio Finos, Gianmarco Altoè, Massimiliano Pastore
In this article we provide initial findings regarding the problem of solving likelihood equations by means of a maximum entropy approach. Unlike standard procedures that require equating at zero the score function of the maximum-likelihood problem, we propose an alternative strategy where the score is instead used as…
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…
Arwa M. Alshangiti, M. Kayid, B. Alarfaj
The purpose of this paper is to provide further study of the Marshall-Olkin log-logistic model that was first described by Gui (Appl Math Sci 7:3947-3961, [8]). This model is both useful and practical in areas such as reliability and life testing. Some statistical and reliability properties of this model are presented…
Max Hill, Sebastien Roch, Jose Israel Rodriguez
Maximum likelihood estimation is among the most widely-used methods for inferring phylogenetic trees from sequence data. This paper solves the problem of computing solutions to the maximum likelihood problem for 3-leaf trees under the 2-state symmetric mutation model (CFN model). Our main result is a closed-form…
Mintodê Nicodème Atchadé, Melchior N’bouké, Aliou Moussa Djibril, Shabnam Shahzadi + 6 more
'Shabnam Shahzadi' 'Eslam Hussam' 'Ramy Aldallal' 'Huda M. Alshanbari' 'Ahmed M. Gemeay' 'Abdal-Aziz H. El-Bagoury' 'Anoop Kumar'] We introduced a brand-new member of the family that is going to be referred to as the New Power Topp-Leone Generated (NPTL-G). This new member is one of a kind. Given the major functions…
C. M. Revathi, Rajesh Moharana
This study proposes the Log-Linear Failure Rate (Log-LFR) distribution, a novel extension of the classical Linear Failure Rate model achieved through a logarithmic transformation. The suggested logarithmic generator is characterized by its survival-based construction, permitting a natural hazard interpretation and an…
Grégory Nuel, Andréa Rau, Florence Jaffrézic
Methodological development for the inference of gene regulatory networks from transcriptomic data is an active and important research area. Several approaches have been proposed to infer relationships among genes from observational steady-state expression data alone, mainly based on the use of graphical Gaussian…
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…
Qian Zhao, Pragya Sur, Emmanuel J. Candès
We study the distribution of the maximum likelihood estimate (MLE) in high-dimensional logistic models, extending the recent results from [14] to the case where the Gaussian covariates may have an arbitrary covariance structure. We prove that in the limit of large problems holding the ratio between the number p of…
Ehsan Fayyazishishavan, Serpil Kılıç Depren, Feng Chen
The two-parameter of exponentiated Gumbel distribution is an important lifetime distribution in survival analysis. This paper investigates the estimation of the parameters of this distribution by using lower records values. The maximum likelihood estimator (MLE) procedure of the parameters is considered, and the Fisher…
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…
Justin B. Kinney, Gurinder S. Atwal
Motivated by data-rich experiments in transcriptional regulation and sensory neuro-science, we consider the following general problem in statistical inference. When exposed to a high-dimensional signal S, a system of interest computes a representation R of that signal which is then observed through a noisy measurement…
Amir Emad Marvasti, Ehsan Emad Marvasti, Ulaş Bağcı, Hassan Foroosh
—We present a theoretical framework of probabilistic learning derived from the Maximum Probability (MP) Theorem shown in the current paper. In this probabilistic framework, a model is defined as an event in the probability space, and a model or the associated event - either the true underlying model or the…
A. E. Allahverdyan
We study the parameter estimation problem in mixture models with observational nonidentifiability: the full model (also containing hidden variables) is identifiable, but the marginal (observed) model is not. Hence global maxima of the marginal likelihood are (infinitely) degenerate and predictions of the marginal…
Oliver J. Maclaren
Profile likelihood is the key tool for dealing with nuisance parameters in likelihood theory. It is often asserted, however, that profile likelihood is not a 'true' likelihood. One implication is that likelihood theory lacks the generality of e.g. Bayesian inference, wherein marginalization is the universal tool for…
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…
Robert Reischke
Confidence contours in parameter space are a helpful tool to compare and classify determined estimators. For more intricate parameter estimations of non-linear nature or complex error structures, the procedure of determining confidence contours is a statistically complex task. For polymer chemists, such particular…
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…
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…
Purushottam D. Dixit
In modern biological physics, there is a great interest in building generative probabilistic models for ensembles of covarying binary variables. A popular approach is to use the maximum entropy principle. Here, one builds generative models that use as constraints lower level statistics estimated from the data. While…
Il Memming Park, Jonathan W. Pillow
The efficient coding hypothesis, which proposes that neurons are optimized to maximize information about the environment, has provided a guiding theoretical framework for sensory and systems neuroscience. More recently, a theory known as the Bayesian Brain hypothesis has focused on the brain’s ability to integrate…
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…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…