16 citations · 41 across the 17 of their papers we have counts for
19 papers
Interpreting relative utility for probabilistic predictions
Linard Hoessly
At a fixed threshold, relative utility (RU) measures the net-benefit gain of a prediction model over the better of treat-all and treat-none relative to the corresponding gain under…
Exact two-sided p-values in natural exponential families: coincidence, non-uniqueness, and sample-size stability
Shaul K. Bar-Lev, Linard Hoessly
We study the non-uniqueness of exact two-sided -values in continuous one-parameter natural exponential families (NEFs). For directed one-sided problems, the tail -value agree…
Why decision curves go above or below treat-all and treat-none: a PPV- and calibration-based guide for clinical prediction models
Linard Hoessly
Net benefit is widely used to assess whether clinical prediction models can improve decision-making, but its interpretation is often difficult in practice. In this didactic note, w…
Identifiability of SDEs for reaction networks
Louis Faul, Linard Hoessly, Panqiu Xia
Biochemical reaction networks are widely applied across scientific disciplines to model complex dynamic systems. We investigate the diffusion approximation of reaction networks wit…
On misconceptions about the Brier score in binary prediction models
Linard Hoessly
The Brier score is a widely used metric evaluating overall performance of probabilistic predictions for binary outcomes in clinical research. However, its interpretation can be com…
Impossibility results for equating the Youden Index with average scoring rules and Tjur -like metrics
Linard Hoessly
We consider the Youden index fas well as measures evaluating predicted probabilities for the maximum-likelihood estimate of a logistic regression model with predictor the classifie…
Reductions of discrete Bayesian networks via lumping
Linard Hoessly
Bayesian networks are widely utilised in various fields, offering elegant representations of factorisations and causal relationships. We use surjective functions to reduce the dime…
Asymptotic analysis for stationary distributions of scaled reaction networks
Linard Hoessly, Carsten Wiuf, Panqiu Xia
We study stationary distributions in the context of stochastic reaction networks. In particular, we are interested in complex balanced reaction networks and reduction of such netwo…
Reaction cleaving and complex-balanced distributions for chemical reaction networks with general kinetics
Linard Hoessly, Carsten Wiuf, Panqiu Xia
Reaction networks have become a major modelling framework in the biological sciences from epidemiology and population biology to genetics and cellular biology. In recent years, muc…
Fast reactions with non-interacting species in stochastic reaction networks
Linard Hoessly, Carsten Wiuf
We consider stochastic reaction networks modeled by continuous-time Markov chains. Such reaction networks often contain many reactions, potentially occurring at different time scal…
On the sum of chemical reactions
Linard Hoessly, Carsten Wiuf, Panqiu Xia
It is standard in chemistry to represent a sequence of reactions by a single overall reaction, often called a complex reaction in contrast to an elementary reaction. Photosynthesis…
Monitoring and Forecasting COVID-19: Statistical Heuristic Regression, Susceptible-Infected-Removed model and, Spatial Stochastics
Pedro L. de Andres, Lucia de Andres-Bragado, Linard D. Hoessly
The COVID-19 pandemic has had worldwide devastating effects on human lives, highlighting the need for tools to predict its development. Dynamics of such public-health threats can o…
An algebraic approach to product-form stationary distributions for some reaction networks
Beatriz Pascual-Escudero, Linard Hoessly
Exact results for product-form stationary distributions of Markov chains are of interest in different fields. In stochastic reaction networks (CRNs), stationary distributions are m…
Stationary distributions via decomposition of stochastic reaction networks
Linard Hoessly
We examine reaction networks (CRNs) through their associated continuous-time Markov processes. Studying the dynamics of such networks is in general hard, both analytically and by s…
Impossibility results on stability of phylogenetic consensus methods
Emanuele Delucchi, Linard Hoessly, Giovanni Paolini
We answer two questions raised by Bryant, Francis and Steel in their work on consensus methods in phylogenetics. Consensus methods apply to every practical instance where it is des…
Stationary distributions and condensation in autocatalytic CRN
Linard Hoessly, Christian Mazza
We investigate a broad family of non weakly reversible stochastically modeled reaction networks (CRN), by looking at their steady-state distributions. Most known results on station…
Mean field repulsive Kuramoto models: Phase locking and spatial signs
Corina Ciobotaru, Linard Hoessly, Christian Mazza +1
The phenomenon of self-synchronization in populations of oscillatory units appears naturally in neurosciences. However, in some situations, the formation of a coherent state is dam…
Fundamental polytopes of metric trees via parallel connections of matroids
Emanuele Delucchi, Linard Hoessly
We tackle the problem of a combinatorial classification of finite metric spaces via their fundamental polytopes, as suggested by Vershik in 2010. In this paper we consider a hyperp…
Realization spaces of matroids over hyperfields
Emanuele Delucchi, Linard Hoessly, Elia Saini
We study realization spaces of matroids over hyperfields (in the sense of Baker and Bowler). More precisely, given a matroid M and a hyperfield H we determine the space of all H-ma…