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1. Unifying Phylogenetic Birth–Death Models in Epidemiology and Macroevolution

   https://doi.org/10.1093/sysbio/syab049  

   MacPherson, Ailene; Louca, Stilianos; McLaughlin, Angela; Joy, Jeffrey B; Pennell, Matthew W (June 2021, Systematic Biology)

   Albert, James (Ed.)

   Abstract Birth–death stochastic processes are the foundations of many phylogenetic models and are widely used to make inferences about epidemiological and macroevolutionary dynamics. There are a large number of birth–death model variants that have been developed; these impose different assumptions about the temporal dynamics of the parameters and about the sampling process. As each of these variants was individually derived, it has been difficult to understand the relationships between them as well as their precise biological and mathematical assumptions. Without a common mathematical foundation, deriving new models is nontrivial. Here, we unify these models into a single framework, prove that many previously developed epidemiological and macroevolutionary models are all special cases of a more general model, and illustrate the connections between these variants. This unification includes both models where the process is the same for all lineages and those in which it varies across types. We also outline a straightforward procedure for deriving likelihood functions for arbitrarily complex birth–death(-sampling) models that will hopefully allow researchers to explore a wider array of scenarios than was previously possible. By rederiving existing single-type birth–death sampling models, we clarify and synthesize the range of explicit and implicit assumptions made by these models. [Birth–death processes; epidemiology; macroevolution; phylogenetics; statistical inference.] 

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2. An empirical Bayes approach to stochastic blockmodels and graphons: shrinkage estimation and model selection

   https://doi.org/10.7717/peerj-cs.1006  

   Peng, Zhanhao; Zhou, Qing (January 2022, PeerJ Computer Science)

   The graphon (W-graph), including the stochastic block model as a special case, has been widely used in modeling and analyzing network data. Estimation of the graphon function has gained a lot of recent research interests. Most existing works focus on inference in the latent space of the model, while adopting simple maximum likelihood or Bayesian estimates for the graphon or connectivity parameters given the identified latent variables. In this work, we propose a hierarchical model and develop a novel empirical Bayes estimate of the connectivity matrix of a stochastic block model to approximate the graphon function. Based on our hierarchical model, we further introduce a new model selection criterion for choosing the number of communities. Numerical results on extensive simulations and two well-annotated social networks demonstrate the superiority of our approach in terms of parameter estimation and model selection. 

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3. Equivalence of ML decoding to a continuous optimization problem

   S. Sundararajan, S N. (January 2020, IEEE International Symposium on Information Theory (ISIT))

   Maximum likelihood (ML) and symbolwise maximum aposteriori (MAP) estimation for discrete input sequences play a central role in a number of applications that arise in communications, information and coding theory. Many instances of these problems are proven to be intractable, for example through reduction to NP-complete integer optimization problems. In this work, we prove that the ML estimation of a discrete input sequence (with no assumptions on the encoder/channel used) is equivalent to the solution of a continuous non-convex optimization problem, and that this formulation is closely related to the computation of symbolwise MAP estimates. This equivalence is particularly useful in situations where a function we term the expected likelihood is efficiently computable. In such situations, we give a ML heuristic and show numerics for sequence estimation over the deletion channel. 

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4. On Assignment to Classes in Latent Class Logit Models

   https://doi.org/10.1177/03611981221121266  

   Wu, Wangwei; Daziano, Ricardo A. (March 2023, Transportation Research Record: Journal of the Transportation Research Board)

   Random parameter logit models address unobserved preference heterogeneity in discrete choice analysis. The latent class logit model assumes a discrete heterogeneity distribution, by combining a conditional logit model of economic choices with a multinomial logit (MNL) for stochastic assignment to classes. Whereas point estimation of latent class logit models is widely applied in practice, stochastic assignment of individuals to classes needs further analysis. In this paper we analyze the statistical behavior of six competing class assignment strategies, namely: maximum prior MNL probabilities, class drawn from prior MNL probabilities, maximum posterior assignment, drawn posterior assignment, conditional individual-specific estimates, and conditional individual estimates combined with the Krinsky–Robb method to account for uncertainty. Using both a Monte Carlo study and two empirical case studies, we show that assigning individuals to classes based on maximum MNL probabilities behaves better than randomly drawn classes in market share predictions. However, randomly drawn classes have higher accuracy in predicted class shares. Finally, class assignment based on individual-level conditional estimates that account for the sampling distribution of the assignment parameters shows superior behavior for a larger number of choice occasions per individual. 

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5. A Millimeter-Wave Channel Simulator NYUSIM with Spatial Consistency and Human Blockage

   https://doi.org/10.1109/globecom38437.2019.9013273  

   Ju, Shihao; Kanhere, Ojas; Xing, Yunchou; Rappaport, Theodore S. (December 2019, 2019 IEEE Global Communications Conference)

   null (Ed.)

   Abstract—Accurate channel modeling and simulation are indispensable for millimeter-wave wideband communication systems that employ electrically-steerable and narrow beam antenna arrays. Three important channel modeling components, spatial consistency, human blockage, and outdoor-to-indoor penetration loss, were proposed in the 3rd Generation Partnership Project Release 14 for mmWave communication system design. This paper presents NYUSIM 2.0, an improved channel simulator which can simulate spatially consistent channel realizations based on the existing drop-based channel simulator NYUSIM 1.6.1. A geometry-based approach using multiple reflection surfaces is proposed to generate spatially correlated and time-variant channel coefficients. Using results from 73 GHz pedestrian measurements for human blockage, a four-state Markov model has been implemented in NYUSIM to simulate dynamic human blockage shadowing loss. To model the excess path loss due to penetration into buildings, a parabolic model for outdoorto- indoor penetration loss has been adopted from the 5G Channel Modeling special interest group and implemented in NYUSIM 2.0. This paper demonstrates how these new modeling capabilities reproduce realistic data when implemented in Monte Carlo fashion using NYUSIM 2.0, making it a valuable measurement-based channel simulator for fifth-generation and beyond mmWave communication system design and evaluation. Index Terms—5G; mmWave; NYUSIM; channel modeling; channel simulator; spatial consistency; human blockage; outdoor-to-indoor loss; building penetration 

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