---
_id: '15020'
abstract:
- lang: eng
text: "This thesis consists of four distinct pieces of work within theoretical biology,
with two themes in common: the concept of optimization in biological systems,
and the use of information-theoretic tools to quantify biological stochasticity
and statistical uncertainty.\r\nChapter 2 develops a statistical framework for
studying biological systems which we believe to be optimized for a particular
utility function, such as retinal neurons conveying information about visual stimuli.
We formalize such beliefs as maximum-entropy Bayesian priors, constrained by the
expected utility. We explore how such priors aid inference of system parameters
with limited data and enable optimality hypothesis testing: is the utility higher
than by chance?\r\nChapter 3 examines the ultimate biological optimization process:
evolution by natural selection. As some individuals survive and reproduce more
successfully than others, populations evolve towards fitter genotypes and phenotypes.
We formalize this as accumulation of genetic information, and use population genetics
theory to study how much such information can be accumulated per generation and
maintained in the face of random mutation and genetic drift. We identify the population
size and fitness variance as the key quantities that control information accumulation
and maintenance.\r\nChapter 4 reuses the concept of genetic information from Chapter
3, but from a different perspective: we ask how much genetic information organisms
actually need, in particular in the context of gene regulation. For example, how
much information is needed to bind transcription factors at correct locations
within the genome? Population genetics provides us with a refined answer: with
an increasing population size, populations achieve higher fitness by maintaining
more genetic information. Moreover, regulatory parameters experience selection
pressure to optimize the fitness-information trade-off, i.e. minimize the information
needed for a given fitness. This provides an evolutionary derivation of the optimization
priors introduced in Chapter 2.\r\nChapter 5 proves an upper bound on mutual information
between a signal and a communication channel output (such as neural activity).
Mutual information is an important utility measure for biological systems, but
its practical use can be difficult due to the large dimensionality of many biological
channels. Sometimes, a lower bound on mutual information is computed by replacing
the high-dimensional channel outputs with decodes (signal estimates). Our result
provides a corresponding upper bound, provided that the decodes are the maximum
posterior estimates of the signal."
acknowledged_ssus:
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
citation:
ama: Hledik M. Genetic information and biological optimization. 2024. doi:10.15479/at:ista:15020
apa: Hledik, M. (2024). Genetic information and biological optimization.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:15020
chicago: Hledik, Michal. “Genetic Information and Biological Optimization.” Institute
of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:15020.
ieee: M. Hledik, “Genetic information and biological optimization,” Institute of
Science and Technology Austria, 2024.
ista: Hledik M. 2024. Genetic information and biological optimization. Institute
of Science and Technology Austria.
mla: Hledik, Michal. Genetic Information and Biological Optimization. Institute
of Science and Technology Austria, 2024, doi:10.15479/at:ista:15020.
short: M. Hledik, Genetic Information and Biological Optimization, Institute of
Science and Technology Austria, 2024.
date_created: 2024-02-23T14:02:04Z
date_published: 2024-02-23T00:00:00Z
date_updated: 2025-06-30T13:21:09Z
day: '23'
ddc:
- '576'
- '519'
department:
- _id: GradSch
- _id: NiBa
- _id: GaTk
doi: 10.15479/at:ista:15020
ec_funded: 1
file:
- access_level: open_access
checksum: b2d3da47c98d481577a4baf68944fe41
content_type: application/pdf
creator: mhledik
date_created: 2024-02-23T13:50:53Z
date_updated: 2024-02-23T13:50:53Z
file_id: '15021'
file_name: hledik thesis pdfa 2b.pdf
file_size: 7102089
relation: main_file
success: 1
- access_level: closed
checksum: eda9b9430da2610fee7ce1c1419a479a
content_type: application/zip
creator: mhledik
date_created: 2024-02-23T13:50:54Z
date_updated: 2024-02-23T14:20:16Z
file_id: '15022'
file_name: hledik thesis source.zip
file_size: 14014790
relation: source_file
file_date_updated: 2024-02-23T14:20:16Z
has_accepted_license: '1'
keyword:
- Theoretical biology
- Optimality
- Evolution
- Information
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
page: '158'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 2665AAFE-B435-11E9-9278-68D0E5697425
grant_number: RGP0034/2018
name: Can evolution minimize spurious signaling crosstalk to reach optimal performance?
- _id: bd6958e0-d553-11ed-ba76-86eba6a76c00
grant_number: '101055327'
name: Understanding the evolution of continuous genomes
publication_identifier:
issn:
- 2663 - 337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '7553'
relation: part_of_dissertation
status: public
- id: '7606'
relation: part_of_dissertation
status: public
- id: '12081'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
title: Genetic information and biological optimization
type: dissertation
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
_id: '12081'
abstract:
- lang: eng
text: 'Selection accumulates information in the genome—it guides stochastically
evolving populations toward states (genotype frequencies) that would be unlikely
under neutrality. This can be quantified as the Kullback–Leibler (KL) divergence
between the actual distribution of genotype frequencies and the corresponding
neutral distribution. First, we show that this population-level information sets
an upper bound on the information at the level of genotype and phenotype, limiting
how precisely they can be specified by selection. Next, we study how the accumulation
and maintenance of information is limited by the cost of selection, measured as
the genetic load or the relative fitness variance, both of which we connect to
the control-theoretic KL cost of control. The information accumulation rate is
upper bounded by the population size times the cost of selection. This bound is
very general, and applies across models (Wright–Fisher, Moran, diffusion) and
to arbitrary forms of selection, mutation, and recombination. Finally, the cost
of maintaining information depends on how it is encoded: Specifying a single allele
out of two is expensive, but one bit encoded among many weakly specified loci
(as in a polygenic trait) is cheap.'
acknowledgement: We thank Ksenia Khudiakova, Wiktor Młynarski, Sean Stankowski, and
two anonymous reviewers for discussions and comments on the manuscript. G.T. and
M.H. acknowledge funding from the Human Frontier Science Program Grant RGP0032/2018.
N.B. acknowledges funding from ERC Grant 250152 “Information and Evolution.”
article_number: e2123152119
article_processing_charge: No
article_type: original
author:
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: '1'
citation:
ama: Hledik M, Barton NH, Tkačik G. Accumulation and maintenance of information
in evolution. Proceedings of the National Academy of Sciences. 2022;119(36).
doi:10.1073/pnas.2123152119
apa: Hledik, M., Barton, N. H., & Tkačik, G. (2022). Accumulation and maintenance
of information in evolution. Proceedings of the National Academy of Sciences.
Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2123152119
chicago: Hledik, Michal, Nicholas H Barton, and Gašper Tkačik. “Accumulation and
Maintenance of Information in Evolution.” Proceedings of the National Academy
of Sciences. Proceedings of the National Academy of Sciences, 2022. https://doi.org/10.1073/pnas.2123152119.
ieee: M. Hledik, N. H. Barton, and G. Tkačik, “Accumulation and maintenance of information
in evolution,” Proceedings of the National Academy of Sciences, vol. 119,
no. 36. Proceedings of the National Academy of Sciences, 2022.
ista: Hledik M, Barton NH, Tkačik G. 2022. Accumulation and maintenance of information
in evolution. Proceedings of the National Academy of Sciences. 119(36), e2123152119.
mla: Hledik, Michal, et al. “Accumulation and Maintenance of Information in Evolution.”
Proceedings of the National Academy of Sciences, vol. 119, no. 36, e2123152119,
Proceedings of the National Academy of Sciences, 2022, doi:10.1073/pnas.2123152119.
short: M. Hledik, N.H. Barton, G. Tkačik, Proceedings of the National Academy of
Sciences 119 (2022).
date_created: 2022-09-11T22:01:55Z
date_published: 2022-08-29T00:00:00Z
date_updated: 2025-06-30T13:21:05Z
day: '29'
ddc:
- '570'
department:
- _id: NiBa
- _id: GaTk
doi: 10.1073/pnas.2123152119
ec_funded: 1
external_id:
isi:
- '000889278400014'
pmid:
- '36037343'
file:
- access_level: open_access
checksum: 6dec51f6567da9039982a571508a8e4d
content_type: application/pdf
creator: dernst
date_created: 2022-09-12T08:08:12Z
date_updated: 2022-09-12T08:08:12Z
file_id: '12091'
file_name: 2022_PNAS_Hledik.pdf
file_size: 2165752
relation: main_file
success: 1
file_date_updated: 2022-09-12T08:08:12Z
has_accepted_license: '1'
intvolume: ' 119'
isi: 1
issue: '36'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
- _id: 2665AAFE-B435-11E9-9278-68D0E5697425
grant_number: RGP0034/2018
name: Can evolution minimize spurious signaling crosstalk to reach optimal performance?
publication: Proceedings of the National Academy of Sciences
publication_identifier:
eissn:
- 1091-6490
issn:
- 0027-8424
publication_status: published
publisher: Proceedings of the National Academy of Sciences
quality_controlled: '1'
related_material:
record:
- id: '15020'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Accumulation and maintenance of information in evolution
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 119
year: '2022'
...
---
_id: '7553'
abstract:
- lang: eng
text: Normative theories and statistical inference provide complementary approaches
for the study of biological systems. A normative theory postulates that organisms
have adapted to efficiently solve essential tasks, and proceeds to mathematically
work out testable consequences of such optimality; parameters that maximize the
hypothesized organismal function can be derived ab initio, without reference to
experimental data. In contrast, statistical inference focuses on efficient utilization
of data to learn model parameters, without reference to any a priori notion of
biological function, utility, or fitness. Traditionally, these two approaches
were developed independently and applied separately. Here we unify them in a coherent
Bayesian framework that embeds a normative theory into a family of maximum-entropy
“optimization priors.” This family defines a smooth interpolation between a data-rich
inference regime (characteristic of “bottom-up” statistical models), and a data-limited
ab inito prediction regime (characteristic of “top-down” normative theory). We
demonstrate the applicability of our framework using data from the visual cortex,
and argue that the flexibility it affords is essential to address a number of
fundamental challenges relating to inference and prediction in complex, high-dimensional
biological problems.
acknowledgement: The authors thank Dario Ringach for providing the V1 receptive fields
and Olivier Marre for providing the retinal receptive fields. W.M. was funded by
the European Union’s Horizon 2020 research and innovation programme under the Marie
Skłodowska-Curie grant agreement no. 754411. M.H. was funded in part by Human Frontiers
Science grant no. HFSP RGP0032/2018.
article_processing_charge: No
author:
- first_name: Wiktor F
full_name: Mlynarski, Wiktor F
id: 358A453A-F248-11E8-B48F-1D18A9856A87
last_name: Mlynarski
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
- first_name: Thomas R
full_name: Sokolowski, Thomas R
id: 3E999752-F248-11E8-B48F-1D18A9856A87
last_name: Sokolowski
orcid: 0000-0002-1287-3779
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. Statistical analysis and optimality
of neural systems. Neuron. 2021;109(7):1227-1241.e5. doi:10.1016/j.neuron.2021.01.020
apa: Mlynarski, W. F., Hledik, M., Sokolowski, T. R., & Tkačik, G. (2021). Statistical
analysis and optimality of neural systems. Neuron. Cell Press. https://doi.org/10.1016/j.neuron.2021.01.020
chicago: Mlynarski, Wiktor F, Michal Hledik, Thomas R Sokolowski, and Gašper Tkačik.
“Statistical Analysis and Optimality of Neural Systems.” Neuron. Cell Press,
2021. https://doi.org/10.1016/j.neuron.2021.01.020.
ieee: W. F. Mlynarski, M. Hledik, T. R. Sokolowski, and G. Tkačik, “Statistical
analysis and optimality of neural systems,” Neuron, vol. 109, no. 7. Cell
Press, p. 1227–1241.e5, 2021.
ista: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. 2021. Statistical analysis
and optimality of neural systems. Neuron. 109(7), 1227–1241.e5.
mla: Mlynarski, Wiktor F., et al. “Statistical Analysis and Optimality of Neural
Systems.” Neuron, vol. 109, no. 7, Cell Press, 2021, p. 1227–1241.e5, doi:10.1016/j.neuron.2021.01.020.
short: W.F. Mlynarski, M. Hledik, T.R. Sokolowski, G. Tkačik, Neuron 109 (2021)
1227–1241.e5.
date_created: 2020-02-28T11:00:12Z
date_published: 2021-04-07T00:00:00Z
date_updated: 2025-06-30T13:21:05Z
day: '07'
department:
- _id: GaTk
doi: 10.1016/j.neuron.2021.01.020
ec_funded: 1
external_id:
isi:
- '000637809600006'
intvolume: ' 109'
isi: 1
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/848374
month: '04'
oa: 1
oa_version: Preprint
page: 1227-1241.e5
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Neuron
publication_status: published
publisher: Cell Press
quality_controlled: '1'
related_material:
link:
- description: News on IST Homepage
relation: press_release
url: https://ist.ac.at/en/news/can-evolution-be-predicted/
record:
- id: '15020'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Statistical analysis and optimality of neural systems
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 109
year: '2021'
...
---
_id: '9816'
abstract:
- lang: eng
text: "Aims: Mass antigen testing programs have been challenged because of an alleged
insufficient specificity, leading to a large number of false positives. The objective
of this study is to derive a lower bound of the specificity of the SD Biosensor
Standard Q Ag-Test in large scale practical use.\r\nMethods: Based on county data
from the nationwide tests for SARS-CoV-2 in Slovakia between 31.10.–1.11. 2020
we calculate a lower confidence bound for the specificity. As positive test results
were not systematically verified by PCR tests, we base the lower bound on a worst
case assumption, assuming all positives to be false positives.\r\nResults: 3,625,332
persons from 79 counties were tested. The lowest positivity rate was observed
in the county of Rožňava where 100 out of 34307 (0.29%) tests were positive. This
implies a test specificity of at least 99.6% (97.5% one-sided lower confidence
bound, adjusted for multiplicity).\r\nConclusion: The obtained lower bound suggests
a higher specificity compared to earlier studies in spite of the underlying worst
case assumption and the application in a mass testing setting. The actual specificity
is expected to exceed 99.6% if the prevalence in the respective regions was non-negligible
at the time of testing. To our knowledge, this estimate constitutes the first
bound obtained from large scale practical use of an antigen test."
acknowledgement: We would like to thank Alfred Uhl, Richard Kollár and Katarína Bod’ová
for very helpful comments. We also thank Matej Mišík for discussion and information
regarding the Slovak testing data and Ag-Test used.
article_number: e0255267
article_processing_charge: Yes
article_type: original
author:
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
- first_name: Jitka
full_name: Polechova, Jitka
id: 3BBFB084-F248-11E8-B48F-1D18A9856A87
last_name: Polechova
orcid: 0000-0003-0951-3112
- first_name: Mathias
full_name: Beiglböck, Mathias
last_name: Beiglböck
- first_name: Anna Nele
full_name: Herdina, Anna Nele
last_name: Herdina
- first_name: Robert
full_name: Strassl, Robert
last_name: Strassl
- first_name: Martin
full_name: Posch, Martin
last_name: Posch
citation:
ama: Hledik M, Polechova J, Beiglböck M, Herdina AN, Strassl R, Posch M. Analysis
of the specificity of a COVID-19 antigen test in the Slovak mass testing program.
PLoS ONE. 2021;16(7). doi:10.1371/journal.pone.0255267
apa: Hledik, M., Polechova, J., Beiglböck, M., Herdina, A. N., Strassl, R., &
Posch, M. (2021). Analysis of the specificity of a COVID-19 antigen test in the
Slovak mass testing program. PLoS ONE. Public Library of Science. https://doi.org/10.1371/journal.pone.0255267
chicago: Hledik, Michal, Jitka Polechova, Mathias Beiglböck, Anna Nele Herdina,
Robert Strassl, and Martin Posch. “Analysis of the Specificity of a COVID-19 Antigen
Test in the Slovak Mass Testing Program.” PLoS ONE. Public Library of Science,
2021. https://doi.org/10.1371/journal.pone.0255267.
ieee: M. Hledik, J. Polechova, M. Beiglböck, A. N. Herdina, R. Strassl, and M. Posch,
“Analysis of the specificity of a COVID-19 antigen test in the Slovak mass testing
program,” PLoS ONE, vol. 16, no. 7. Public Library of Science, 2021.
ista: Hledik M, Polechova J, Beiglböck M, Herdina AN, Strassl R, Posch M. 2021.
Analysis of the specificity of a COVID-19 antigen test in the Slovak mass testing
program. PLoS ONE. 16(7), e0255267.
mla: Hledik, Michal, et al. “Analysis of the Specificity of a COVID-19 Antigen Test
in the Slovak Mass Testing Program.” PLoS ONE, vol. 16, no. 7, e0255267,
Public Library of Science, 2021, doi:10.1371/journal.pone.0255267.
short: M. Hledik, J. Polechova, M. Beiglböck, A.N. Herdina, R. Strassl, M. Posch,
PLoS ONE 16 (2021).
date_created: 2021-08-08T22:01:26Z
date_published: 2021-07-29T00:00:00Z
date_updated: 2023-08-10T14:26:32Z
day: '29'
ddc:
- '610'
department:
- _id: NiBa
doi: 10.1371/journal.pone.0255267
external_id:
isi:
- '000685248200095'
pmid:
- '34324553'
file:
- access_level: open_access
checksum: ae4df60eb62f4491278588548d0c1f93
content_type: application/pdf
creator: asandaue
date_created: 2021-08-09T11:52:14Z
date_updated: 2021-08-09T11:52:14Z
file_id: '9835'
file_name: 2021_PLoSONE_Hledík.pdf
file_size: 773921
relation: main_file
success: 1
file_date_updated: 2021-08-09T11:52:14Z
has_accepted_license: '1'
intvolume: ' 16'
isi: 1
issue: '7'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLoS ONE
publication_identifier:
eissn:
- 1932-6203
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Analysis of the specificity of a COVID-19 antigen test in the Slovak mass testing
program
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 16
year: '2021'
...
---
_id: '7606'
abstract:
- lang: eng
text: We derive a tight lower bound on equivocation (conditional entropy), or equivalently
a tight upper bound on mutual information between a signal variable and channel
outputs. The bound is in terms of the joint distribution of the signals and maximum
a posteriori decodes (most probable signals given channel output). As part of
our derivation, we describe the key properties of the distribution of signals,
channel outputs and decodes, that minimizes equivocation and maximizes mutual
information. This work addresses a problem in data analysis, where mutual information
between signals and decodes is sometimes used to lower bound the mutual information
between signals and channel outputs. Our result provides a corresponding upper
bound.
article_number: '8989292'
article_processing_charge: No
arxiv: 1
author:
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
- first_name: Thomas R
full_name: Sokolowski, Thomas R
id: 3E999752-F248-11E8-B48F-1D18A9856A87
last_name: Sokolowski
orcid: 0000-0002-1287-3779
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: 'Hledik M, Sokolowski TR, Tkačik G. A tight upper bound on mutual information.
In: IEEE Information Theory Workshop, ITW 2019. IEEE; 2019. doi:10.1109/ITW44776.2019.8989292'
apa: 'Hledik, M., Sokolowski, T. R., & Tkačik, G. (2019). A tight upper bound
on mutual information. In IEEE Information Theory Workshop, ITW 2019. Visby,
Sweden: IEEE. https://doi.org/10.1109/ITW44776.2019.8989292'
chicago: Hledik, Michal, Thomas R Sokolowski, and Gašper Tkačik. “A Tight Upper
Bound on Mutual Information.” In IEEE Information Theory Workshop, ITW 2019.
IEEE, 2019. https://doi.org/10.1109/ITW44776.2019.8989292.
ieee: M. Hledik, T. R. Sokolowski, and G. Tkačik, “A tight upper bound on mutual
information,” in IEEE Information Theory Workshop, ITW 2019, Visby, Sweden,
2019.
ista: Hledik M, Sokolowski TR, Tkačik G. 2019. A tight upper bound on mutual information.
IEEE Information Theory Workshop, ITW 2019. Information Theory Workshop, 8989292.
mla: Hledik, Michal, et al. “A Tight Upper Bound on Mutual Information.” IEEE
Information Theory Workshop, ITW 2019, 8989292, IEEE, 2019, doi:10.1109/ITW44776.2019.8989292.
short: M. Hledik, T.R. Sokolowski, G. Tkačik, in:, IEEE Information Theory Workshop,
ITW 2019, IEEE, 2019.
conference:
end_date: 2019-08-28
location: Visby, Sweden
name: Information Theory Workshop
start_date: 2019-08-25
date_created: 2020-03-22T23:00:47Z
date_published: 2019-08-01T00:00:00Z
date_updated: 2025-06-30T13:21:05Z
day: '01'
department:
- _id: GaTk
doi: 10.1109/ITW44776.2019.8989292
ec_funded: 1
external_id:
arxiv:
- '1812.01475'
isi:
- '000540384500015'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1812.01475
month: '08'
oa: 1
oa_version: Preprint
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: IEEE Information Theory Workshop, ITW 2019
publication_identifier:
isbn:
- '9781538669006'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '15020'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: A tight upper bound on mutual information
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2019'
...