Zobrazeno 1 - 10
of 420
pro vyhledávání: '"Gael, M."'
Theoretical studies of giant planet formation suggest that substantial quantities of metals - elements heavier than hydrogen and helium - can be delivered by solid accretion during the envelope-assembly phase. This metal enhancement process is believ
Externí odkaz:
http://arxiv.org/abs/2409.13651
Comparing transit spectroscopy pipelines at the catalogue level: evidence for systematic differences
Publikováno v:
MNRAS, Volume 531, Issue 1, June 2024, Pages 35-51
The challenge of inconsistent results from different data pipelines, even when starting from identical data, is a recognized concern in exoplanetary science. As we transition into the James Webb Space Telescope (JWST) era and prepare for the ARIEL sp
Externí odkaz:
http://arxiv.org/abs/2402.15803
Approximate Bayesian Computation (ABC) has gained popularity as a method for conducting inference and forecasting in complex models, most notably those which are intractable in some sense. In this paper we use ABC to produce probabilistic forecasts i
Externí odkaz:
http://arxiv.org/abs/2311.01021
We demonstrate that the forecasting combination puzzle is a consequence of the methodology commonly used to produce forecast combinations. By the combination puzzle, we refer to the empirical finding that predictions formed by combining multiple fore
Externí odkaz:
http://arxiv.org/abs/2308.05263
This paper explores the implications of producing forecast distributions that are optimized according to scoring rules that are relevant to financial risk management. We assess the predictive performance of optimal forecasts from potentially misspeci
Externí odkaz:
http://arxiv.org/abs/2303.01651
Autor:
Martin, Gael M., Frazier, David T., Maneesoonthorn, Worapree, Loaiza-Maya, Ruben, Huber, Florian, Koop, Gary, Maheu, John, Nibbering, Didier, Panagiotelis, Anastasios
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem -- model, parameters, latent states -- is able to be quantified expli
Externí odkaz:
http://arxiv.org/abs/2212.03471
Autor:
The JWST Transiting Exoplanet Community Early Release Science Team, Ahrer, Eva-Maria, Alderson, Lili, Batalha, Natalie M., Batalha, Natasha E., Bean, Jacob L., Beatty, Thomas G., Bell, Taylor J., Benneke, Björn, Berta-Thompson, Zachory K., Carter, Aarynn L., Crossfield, Ian J. M., Espinoza, Néstor, Feinstein, Adina D., Fortney, Jonathan J., Gibson, Neale P., Goyal, Jayesh M., Kempton, Eliza M. -R., Kirk, James, Kreidberg, Laura, López-Morales, Mercedes, Line, Michael R., Lothringer, Joshua D., Moran, Sarah E., Mukherjee, Sagnick, Ohno, Kazumasa, Parmentier, Vivien, Piaulet, Caroline, Rustamkulov, Zafar, Schlawin, Everett, Sing, David K., Stevenson, Kevin B., Wakeford, Hannah R., Allen, Natalie H., Birkmann, Stephan M., Brande, Jonathan, Crouzet, Nicolas, Cubillos, Patricio E., Damiano, Mario, Désert, Jean-Michel, Gao, Peter, Harrington, Joseph, Hu, Renyu, Kendrew, Sarah, Knutson, Heather A., Lagage, Pierre-Olivier, Leconte, Jérémy, Lendl, Monika, MacDonald, Ryan J., May, E. M., Miguel, Yamila, Molaverdikhani, Karan, Moses, Julianne I., Murray, Catriona Anne, Nehring, Molly, Nikolov, Nikolay K., de la Roche, D. J. M. Petit dit, Radica, Michael, Roy, Pierre-Alexis, Stassun, Keivan G., Taylor, Jake, Waalkes, William C., Wachiraphan, Patcharapol, Welbanks, Luis, Wheatley, Peter J., Aggarwal, Keshav, Alam, Munazza K., Banerjee, Agnibha, Barstow, Joanna K., Blecic, Jasmina, Casewell, S. L., Changeat, Quentin, Chubb, K. L., Colón, Knicole D., Coulombe, Louis-Philippe, Daylan, Tansu, de Val-Borro, Miguel, Decin, Leen, Santos, Leonardo A. Dos, Flagg, Laura, France, Kevin, Fu, Guangwei, Muñoz, A. García, Gizis, John E., Glidden, Ana, Grant, David, Heng, Kevin, Henning, Thomas, Hong, Yu-Cian, Inglis, Julie, Iro, Nicolas, Kataria, Tiffany, Komacek, Thaddeus D., Krick, Jessica E., Lee, Elspeth K. H., Lewis, Nikole K., Lillo-Box, Jorge, Lustig-Yaeger, Jacob, Mancini, Luigi, Mandell, Avi M., Mansfield, Megan, Marley, Mark S., Mikal-Evans, Thomas, Morello, Giuseppe, Nixon, Matthew C., Ceballos, Kevin Ortiz, Piette, Anjali A. A., Powell, Diana, Rackham, Benjamin V., Ramos-Rosado, Lakeisha, Rauscher, Emily, Redfield, Seth, Rogers, Laura K., Roman, Michael T., Roudier, Gael M., Scarsdale, Nicholas, Shkolnik, Evgenya L., Southworth, John, Spake, Jessica J., Steinrueck, Maria E, Tan, Xianyu, Teske, Johanna K., Tremblin, Pascal, Tsai, Shang-Min, Tucker, Gregory S., Turner, Jake D., Valenti, Jeff A., Venot, Olivia, Waldmann, Ingo P., Wallack, Nicole L., Zhang, Xi, Zieba, Sebastian
Carbon dioxide (CO2) is a key chemical species that is found in a wide range of planetary atmospheres. In the context of exoplanets, CO2 is an indicator of the metal enrichment (i.e., elements heavier than helium, also called "metallicity"), and thus
Externí odkaz:
http://arxiv.org/abs/2208.11692
Publikováno v:
Statistical Science, 2023
This paper takes the reader on a journey through the history of Bayesian computation, from the 18th century to the present day. Beginning with the one-dimensional integral first confronted by Bayes in 1763, we highlight the key contributions of: Lapl
Externí odkaz:
http://arxiv.org/abs/2208.00646
We investigate the performance and sampling variability of estimated forecast combinations, with particular attention given to the combination of forecast distributions. Unknown parameters in the forecast combination are optimized according to criter
Externí odkaz:
http://arxiv.org/abs/2206.02376
Autor:
Pesonen, Henri, Simola, Umberto, Köhn-Luque, Alvaro, Vuollekoski, Henri, Lai, Xiaoran, Frigessi, Arnoldo, Kaski, Samuel, Frazier, David T., Maneesoonthorn, Worapree, Martin, Gael M., Corander, Jukka
Approximate Bayesian computation (ABC) has advanced in two decades from a seminal idea to a practically applicable inference tool for simulator-based statistical models, which are becoming increasingly popular in many research domains. The computatio
Externí odkaz:
http://arxiv.org/abs/2112.12841