activity
20202026
most citedGeneralized Parallel Tempering on Bayesian Inverse Problems

12 citations · 13 across the 5 of their papers we have counts for

collaborators

6 papers

cs.NE2026

Evolutionary Ensemble Search: Council-Guided Program Evolution with Persistent Memory

Juan P. Madrigal-Cianci, Eshan Chordia

Evolutionary Ensemble Search (EES) constructs machine-learning procedures through expert-guided program evolution. A role-specialized council turns task evidence and experimental r…

q-fin.MF2026

Prediction Markets as Bayesian Inverse Problems: Uncertainty Quantification, Identifiability, and Information Gain from Price-Volume Histories under Latent Types

Juan Pablo Madrigal-Cianci, Camilo Monsalve Maya, Lachlan Breakey

Prediction markets are often described as mechanisms that ``aggregate information'' into prices, yet the mapping from dispersed private information to observed market histories is…

cs.CR2023

Smoothening block rewards: How much should miners pay for mining pools?

Axel Cortes-Cubero, Juan P. Madrigal-Cianci, Kiran Karra +1

The rewards a blockchain miner earns vary with time. Most of the time is spent mining without receiving any rewards, and only occasionally the miner wins a block and earns a reward…

cs.CR2023

An Agent-Based Model Framework for Utility-Based Cryptoeconomies

Kiran Karra, Tom Mellan, Maria Silva +3

In this paper, we outline a framework for modeling utility-based blockchain-enabled economic systems using Agent Based Modeling (ABM). Our approach is to model the supply dynamics…

math.NA2021★ 1 cited

Analysis of a class of Multi-Level Markov Chain Monte Carlo algorithms based on Independent Metropolis-Hastings

Juan Pablo Madrigal-Cianci, Fabio Nobile, Raul Tempone

In this work, we present, analyze, and implement a class of Multi-Level Markov chain Monte Carlo (ML-MCMC) algorithms based on independent Metropolis-Hastings proposals for Bayesia…

math.NA2020★ 12 cited

Generalized Parallel Tempering on Bayesian Inverse Problems

Jonas Latz, Juan P. Madrigal-Cianci, Fabio Nobile +1

In the current work we present two generalizations of the Parallel Tempering algorithm, inspired by the so-called continuous-time Infinite Swapping algorithm. Such a method, found…